# Monty Hall problem

In search of a new car, the player picks door 1. The game host then opens door 3 to reveal a goat and offers to let the player pick door 2 instead of door 1.
The Monty Hall problem is a puzzle involving probability loosely based on the American game show Let's Make a Deal. The name comes from the show's host, Monty Hall. A widely known statement of the problem appeared in a letter to Marilyn vos Savant's Ask Marilyn column in Parade (vos Savant 1990):
Suppose you're on a game show, and you're given the choice of three doors: Behind one door is a car; behind the others, goats. You pick a door, say No. 1, and the host, who knows what's behind the doors, opens another door, say No. 3, which has a goat. He then says to you, "Do you want to pick door No. 2?" Is it to your advantage to switch your choice?

Because there is no way for the player to know which of the two unopened doors is the winning door, many people assume that each door has an equal probability and conclude that switching does not matter. However, as long as the host knows what is behind each door, always opens a door revealing a goat, and always makes the offer to switch, opening a losing door does not change the probability of 1/3 that the car is behind the player's initially chosen door. As there is only one other unopened door, the probability that this door conceals the car must be 2/3. It is therefore to the contestant's advantage to switch to door 2.

The problem is also called the Monty Hall paradox; it is a veridical paradox in the sense that the solution is counterintuitive. For example, when the problem and the correct solution appeared in Parade, approximately 10,000 readers, including several hundred mathematics professors, wrote to the magazine claiming the published solution was wrong. Some of the controversy was because the Parade statement of the problem fails to fully specify the host's behavior and is thus technically ambiguous. However, even when given completely unambiguous problem statements, explanations, simulations, and formal mathematical proofs, many people still meet the correct answer with disbelief.

## Problem and solution

### Problem

The statement of the problem in the Ask Marilyn column in Parade leaves critical aspects of the host's behavior unstated, making the problem mathematically ambiguous. In a more precise statement of the problem (Mueser and Granberg 1999) the host is constrained to always open a door revealing a goat and to always make the offer to switch:
A thoroughly honest game-show host has placed a car behind one of three doors. There is a goat behind each of the other doors. You have no prior knowledge that allows you to distinguish among the doors. "First you point toward a door," he says. "Then I'll open one of the other doors to reveal a goat. After I've shown you the goat, you make your final choice whether to stick with your initial choice of doors, or to switch to the remaining door. You win whatever is behind the door." You begin by pointing to door number 1. The host shows you that door number 3 has a goat.

Do the player's chances of getting the car increase by switching to Door 2?

The problem as generally intended also assumes that the particular door the host opens conveys no special information about whether the player's initial choice is correct. The simplest way to make this explicit is to add a constraint that the host will open one of the remaining two doors randomly if the player initially picked the car.

### Solution

Once the host has opened a door, the car must be behind one of the two remaining doors. The player has no way to know which of these doors is the winning door, leading many people to assume that each door has an equal probability and to conclude that switching does not matter (Mueser and Granberg, 1999). This "equal probability" assumption, while being intuitively seductive, is incorrect. The player's chances of winning the car actually double by switching to the door the host offers.

The chance of initially choosing the car is one in three, which is the chance of winning the car by sticking with this choice. By contrast, the chance of initially choosing a door with a goat is two in three, and a player originally choosing a door with a goat wins by switching. In both cases the host must reveal a goat. In the 2/3 case where the player initially chooses a goat, the host must reveal the other goat making the only remaining door the one with the car.

1.
 Host revealsGoat AorHost revealsGoat B
Player picks carSwitching loses.
2.
Host must
reveal Goat B

Player picks Goat ASwitching wins.
3.
Host must
reveal Goat A

Player picks Goat BSwitching wins.
The player has an equal chance of initially selecting the car, Goat A, or Goat B. Switching results in a win 2/3 of the time.

More formally, when the player is asked whether to switch there are three possible situations corresponding to the player's initial choice, each with probability 1/3:
• The player originally picked the door hiding the car. The game host has shown one of the two goats.
• The player originally picked the door hiding Goat A. The game host has shown the other goat.
• The player originally picked the door hiding Goat B. The game host has shown the other goat.
If the player chooses to switch, the player wins the car in the last two cases. A player choosing to stay with the initial choice wins in only the first case. Since in two out of three equally likely cases switching wins, the probability of winning by switching is 2/3. In other words, players who switch will win the car on average two times out of three.

The solution would be different if the host did not know what was behind each door, or if the host sometimes had the option of not offering the player the chance to switch. Some statements of the problem, notably the one in Parade Magazine, do not explicitly exclude these possibilities. For example, if the game host only offers the opportunity to switch if the contestant originally chooses the car, the probability of winning by switching is 0%. In the common understanding of the problem as stated by Mueser and Granberg, the host must reveal a goat and must make the offer to switch so the player has a 2/3 chance of winning by switching.

## Aids to understanding

### Why the probability is 2/3

The most commonly voiced objection to the solution is that the past can be ignored when assessing the probability — that it is irrelevant which doors the player initially picks and the host opens. However, in the problem as originally presented, the player's initial choice does influence the host's available choices.

This difference can be demonstrated by contrasting the original problem with a variation that appeared in vos Savant's column in November 2006. In this version, Monty Hall forgets which door hides the car. He opens one of the doors at random and is relieved when a goat is revealed. Asked whether the contestant should switch, vos Savant correctly replied, "If the host is clueless, it makes no difference whether you stay or switch. If he knows, switch" (vos Savant, 2006).

In this version of the puzzle, the player has an equal chance of winning whether switching or not. There are six possible sequences of events that can occur, each with probability 1/6:

Player picks Host reveals Third door contains
Goat ACarGoat B
Goat BCarGoat A
Goat AGoat BCar
Goat BGoat ACar
CarGoat AGoat B
CarGoat BGoat A

In the first two cases above, the host reveals the car. What might happen in these cases is unknown — perhaps the contestant immediately wins or immediately loses. However, in the problem as stated, the host has revealed a goat, so only four of the six cases remain possible, and they are equally likely. In two of these four cases, switching results in a win, and in the other two, switching results in a goat. Staying with the original pick gives the same odds: a loss in two cases and a win in two others.

The player's probability of winning by switching increases to 2/3 in the problem as stated by Mueser and Granberg because in the two cases above where the host would reveal the car, he is forced to reveal the remaining goat instead. In the table below, the host's picks from the table above are highlighted. Because he cannot reveal the car, his behavior is altered in two cases:

Player picks Host reveals Third door contains
Goat AGoat BCar
Goat BGoat ACar
Goat AGoat BCar
Goat BGoat ACar
CarGoat AGoat B
CarGoat BGoat A

This change in the host's behavior causes the car to be twice as likely to be behind the "third door", and is what causes switching to be twice as likely to win in the "host knows" variation of the problem.

### Sources of confusion

One reason the Monty Hall problem may be so counterintuitive is that the host is expected to be deceitful (Mueser and Granberg 1999). If the host opens a door only when the player has chosen correctly, then when the host opens a door the player should never opt to switch.

Another possible reason for confusion is that the problem is often stated as though the host takes the player by surprise by opening the door and offering the choice. This tends to give the impression that the host is trying to confuse a player who has chosen correctly, and would mean the player did not know the rules in advance. If the player did not know the rules, that would not alter the probability in the particular case, but it would mean that the player could not definitively make the optimal choice. This confusion is dealt with in the unambiguous statement of the problem where the host explicitly relates the rules to the contestant in advance.

A third possible reason for confusion could come from how the problem is presented. If either the assumption that the host knows which door the car is behind or the assumption that the host always offers the choice to switch is omitted, then choosing to switch doors or to stay with the original decision yields an indeterminate probability for success.

### Increasing the number of doors

It may be easier to appreciate the solution by considering the same problem with 100 doors instead of just three. In this case there are 99 doors with goats behind them and one door with a prize. The player picks a door. The game host then opens 98 of the other doors revealing 98 goats — imagine the host starting with the first door and going down a line of 100 doors, opening each one, skipping over only the player's door and one other door. The host then offers the player the chance to switch to the only other unopened door. On average, in 99 out of 100 times the other door will contain the prize, as 99 out of 100 times the player first picked a door with a goat. A rational player should switch.

The three-door game, by comparison, gives a misleading impression, because the player is always presented with 1/3 proportions. There is a 1/3 chance of winning, the host reveals 1/3 of the mystery, and the player is allowed to switch to the other 1/3 option. All options seem equal — yet they are not. This is an essential ingredient for the counter-intuitiveness of the original problem.

Even if only one of the 100 doors is opened, switching still increases the player's chances of finding a car. The 99/100 chance that the car is not behind the door the player picked is spread evenly over 98 doors after the host reveals one goat. Each of those 98 doors, that is all doors other than the one the player picked and the one the host reveals, has a 99/9800 chance of having the car, so by switching the player slightly improves the chances of winning the car — from a 0.0100 (1/100) chance to just over 0.0101 (99/9800). This is an improvement by a factor of 99/98, or slightly more than 1% better odds.

The same algorithm can be followed for any number of doors, N. The algorithm is "Choose a door, eliminate some number (0 < x < N-1) of the remaining, losing doors, decide to switch or not." This algorithm can be followed for N = 3 or N = 100. The higher N values demonstrate the same mathematical principle in a more obvious way.

### Venn diagram

The probability that the car is behind the remaining door can be calculated using Venn diagrams. After choosing Door 1, for example, the player has a 1/3 chance of having selected the door with the car, leaving a 2/3 chance between the other two doors, as shown below. Note that there is a 100% chance of finding a goat behind at least one of the two unchosen doors because there is only one car.

The host now opens Door 3. Since the host knows what is behind the doors and must always open a door revealing a goat, opening this door does not affect the chance that the car is behind the originally chosen door which remains 1/3. The car is not behind Door 3, so the entire 2/3 probability of the two unchosen doors is now carried only by Door 2, as shown below. Another way to state this is that if the car is behind either door 2 or 3, by opening Door 3 the host has revealed it must be behind Door 2.

### Decision tree

Tree listing the probability of every possible outcome
More formally, the scenario can be depicted in a decision tree.

In the first two cases, wherein the player has first chosen a goat, switching will yield the car. In the third and fourth cases, since the player has chosen the car initially, a switch will lead to a goat.

The total probability that switching wins is equal to the sum of the first two events, 1/3 + 1/3 =2/3. Likewise, the probability that staying wins is 1/6 + 1/6 =1/3.

### Combining doors

Instead of one door being opened and shown to be a losing door, an equivalent action is to combine the two unchosen doors into one since the player cannot, and will not, choose the opened door. The player therefore has the choice of either sticking with the original choice of door with a 1/3 chance of winning the car, or choosing the sum of the contents of the two other doors with a 2/3 chance. The game assumptions play a role here — switching is equivalent to taking the combined contents because the game host is required to open a door with a goat.

In this case what should be ignored is the opening of the door. The player actually chooses between the originally picked door and the other two — opening one is simply a distraction. There is only one car and it does not move. The original choice divides the possible locations of the car between the one door the player picks with a 1/3 chance and the other two with a 2/3 chance. It is already known that at least one of the two unpicked doors contains a goat. Revealing the goat therefore gives the player no additional information about the originally chosen door; it does not change the 2/3 probability that the car is still in the block of two doors.

### Bayes' theorem

An analysis of the problem using the formalism of Bayesian probability theory (Gill 2002) makes explicit the role of the assumptions underlying the problem. In Bayesian terms, probabilities are associated to propositions, and express a degree of belief in their truth, subject to whatever background information happens to be known. For this problem the background is the set of game rules, and the propositions of interest are:

: The car is behind Door i, for i equal to 1, 2 or 3.

: The host opens Door j after the player has picked Door i, for i and j equal to 1, 2 or 3.

For example, denotes the proposition the car is behind Door 1, and denotes the proposition the host opens Door 2 after the player has picked Door 1. Indicating the background information with , the assumptions are formally stated as follows.

First, the car can be behind any door, and all doors are a priori equally likely to hide the car. In this context a priori means before the game is played, or before seeing the goat. Hence, the prior probability of a proposition is:

.

Second, the host will always open a door that has no car behind, chosen among the two not picked by the player. If two such doors are available, each one is equally likely to be opened. This rule determines the conditional probability of a proposition subject to where the car is, i.e. conditioned on a proposition . Specifically, it is:

 if i = j, (the host cannot open the door picked by the player) if j = k, (the host cannot open a door with a car behind it) if i = k, (the two doors with no car are equally likely to be opened) if i ≠k and j ≠ k, (there is only one door available to open)

The problem can now be solved by scoring each strategy with its associated posterior probability of winning, that is with its probability subject to the host's opening of one of the doors. Without loss of generality assume, by re-numbering the doors if necessary, that the player picks Door 1, and the host then opens Door 3, showing him or her a goat. In other words, the host makes proposition true.

The posterior probability of winning by not switching doors, subject to the game rules and , is then . Using Bayes' theorem this is expressed as:

.

By the assumptions stated above, the numerator of the right-hand side is:

.

The normalizing constant at the denominator can be evaluated by expanding it using the definitions of marginal probability and conditional probability:

Dividing the numerator by the normalizing constant yields:

.

Note that this is equal to the prior probability for the car to be behind the initially chosen door, meaning that the host's action has not contributed any novel information with regard to this eventuality. In fact, the following argument shows that the effect of the host's action consists entirely of redistributing the probabilities for the car to be behind any of the other two doors.

The probability of winning by switching the selection to Door 2, , can be evaluated by requiring that the posterior probabilities of all the propositions add to 1. That is:

.

There is no car behind Door 3 since the host opened it, so the last term must be zero. This can be proven using Bayes' theorem and the previous results:

Hence:

.

This shows that the winning strategy is to switch the selection to Door 2. It also makes clear the host's showing of the goat at Door 3 has the effect of transferring the 1/3 of winning probability a-priori associated to that door to the remaining unselected and unopened one, thus making it the most likely winning choice.

### Bayes' explanation

Judea Pearl's book (1988) gives a Bayesian explanation for people's tendency to provide the (wrong) answer 1/2. After the hosts reveals that a goat is behind door 3, people tend to condition their beliefs on the revealed information "a goat is behind door 3" and obtain the answer:

.
The correct answer is obtained by conditioning on the total evidence available: "host revealed a goat behind door 3," giving:

.

The distinction between "information revealed" and "total evidence" has far reaching implications in reasoning under uncertainty [Pearl, 1990, 1992]

### Simulation

A simple way to demonstrate that a switching strategy really does win two out of three times on the average is to simulate the game with playing cards. Three cards from an ordinary deck are used to represent the three doors; one 'special' card such as the Ace of Spades should represent the door with the car, and ordinary cards, such as the two red deuces, represent the goat doors.

The simulation, using the following procedure, can be repeated several times to simulate multiple rounds of the game. One card is dealt at random to the 'player', to represent the door the player picks initially. Then, looking at the remaining two cards at least one of which must be a red two, the 'host' discards a red two. If the card remaining in the host's hand is the Ace of Spades, this is recorded as a round where the player would have won by switching; if the host is holding a red two, the round is recorded as one where staying would have won.

By the law of large numbers, this experiment is likely to approximate the probability of winning, and running the experiment over enough rounds should not only verify that the player does win by switching two times out of three, but show why. Two times out of three, after one card has been dealt to the player, the Ace of Spades is in the host's hand. At that point, it is already determined whether staying or switching will win the round for the player.

If this is not convincing, the simulation can be done with the entire deck, dealing one card to the player and keeping the other 51. In this variant the Ace of Spades goes to the host 51 times out of 52, and stays with the host no matter how many non-Ace cards are discarded.

## Variants

### Other host behaviors

In some versions of the Monty Hall problem the host's behavior is not fully specified. For example, the version published in Parade in 1990 did not specifically state that the host would always open another door, or always offer a choice to switch, or even never open the door revealing the car. Without specifying these rules, the player does not have enough information to conclude switching will be successful two-thirds of the time. The table shows possible host behaviors and the impact on the success of switching. In the majority of these listed host behaviors, switching is more often successful.

Possible host behaviors in unspecified problem
Host behavior Result
The host only offers the option to switch when the player's initial choice is the winning door.Switching always yields a goat.
The host only offers the option to switch when the player has chosen incorrectly.Switching always wins the car.
The host does not know what lies behind the doors, and the player loses if the host reveals the car.The player loses when the car is revealed a third of the time. If the prize is still hidden, switching wins the car half of the time.
The host has the option of opening the door that the player picked.The player loses when the car is revealed a third of the time. If the prize is still hidden, switching wins the car half of the time.
The host acts as noted in the specific version of the problem.Switching wins the car two-thirds of the time.

Another analysis considers three types of hosts and three prize levels. The Benevolent Host always shows the worst remaining prize after you choose, the Random Host randomly picks a remaining door to show, and the Malevolent Host always shows the best remaining prize. The prizes are bad, middle, best; e.g. Goat, Luggage, Car. The Player is unaware of which prize is which. He may expect to be choosing among Pigs, Goats, Blenders, Luggage, Cars, and Houses.

If you always switch, the results for each host are:
Results after switching, expanded behaviors
Benevolent Host 0%33%67%
Random Host33%33%33%
Malevolent Host67%33%0%
IF each host were equally likely, the total probability for each prize becomes 33%, the same as not switching. Without knowing the type of Host and the prize mix, you can make no real statement about the success of a switching strategy.

The only way to "improve the odds” is if the Player can get some meaningful information from the prize shown. I.e. IF you know what the three prizes are AND what type of Host you have, THEN you can develop a winning strategy. If you were wrong about the Host OR the prize mix, that strategy may be harmful.

### Two players

In this variant two players are each allowed to choose a different door. The game host eliminates a player who has chosen a door hiding a goat; if either player has chosen the car the other is eliminated, otherwise one of the players is eliminated at random. The host then opens the eliminated player's door, and offers the remaining player a chance to switch to the originally unchosen door. Should the remaining player switch?

The answer is no. Switching in this game wins if and only if both players originally picked goats; the likelihood of this is only 1/3. By sticking with the original choice the remaining player wins in the remaining 2/3 of the cases. So stickers will win twice as often as switchers.

There are three possible scenarios, all with probability 1/3:
• Player 1 picks the door with the car. The host must eliminate player 2. Switching loses.
• Player 2 picks the door with the car. The host must eliminate player 1. Switching loses.
• Neither player picks the car. The host eliminates one of the players randomly. Switching wins.
Player 1 is the remaining player in the first case and half the time in the third case. Switching loses twice as often as it wins: 1/3 chance of being the remaining player and switching losing vs. 1/6 chance of remaining and switching winning. Similarly, player 2 is the remaining player in the second case and half the time in the third, and also loses twice as often by switching. Regardless of which player remains, this player has a 2/3 probability of winning with the sticking strategy.

The two player game, from the final player's point of view, resembles the single player game: the player chooses a door, a goat is revealed behind another door, and the player is given the opportunity to switch. However, the significant difference is that one player is eliminated. The process of surviving the elimination improves the remaining player's chances of having chosen the car from 1/3 to 2/3. Another way to look at this is that the chances of the remaining player having not chosen the car initially is a combined probability: it is the chance of not choosing the car initially and not being the eliminated player: (2/3)x(1/2) =1/3. The only other scenario for the remaining player is choosing the car and since the two possible outcomes must have a probability of 1 the probability of having the car is now 2/3.

The two player game is exactly the same as the one player game, except in reverse. In the one player game, it is the player's chosen door that is guaranteed not to be opened, and which therefore retains the original probability of 1/3. In the two person game, it is the unchosen door that is guaranteed not to be opened, and which therefore retains the original probability. If there were a three person game, in which one of the goat doors is randomly chosen, then no door can be categorized as guaranteed not to be opened, and therefore none of them retain the original probability of 1/3. In such a game, there is a true symmetry between the doors, and there would be no benefit to either switching or not switching.

### Sequential doors

There is a generalization of the original problem to n doors. In the first step, the player chooses a door. The game host then opens some other door that is a loser. If desired, the player may then switch to another door. The game host then opens another as-yet-unopened losing door, different from the player's current choice. Then the player may switch again, and so on. This continues until there are only two unopened doors left: the player's current choice and another one. How many times should the player switch, and when, if at all?

One possible strategy is to stick with the first choice all the way through but then switch at the very end. With four doors, this strategy can be proven optimal; it has been asserted that with n doors, this strategy is also optimal and gives a probability of winning equal to (n−1)/n (Bapeswara Rao and Rao 1992).

This problem appears similar to the television show Deal or No Deal, which typically begins with 26 boxes. The player selects one to keep, and then randomly picks boxes to open from amongst the rest. In this game, even until the end, the box the player initially selects and all boxes left unrevealed are equally likely to be the winner. The distinction is that any box the player picks to open might reveal the grand prize, thereby eliminating it from contention. Monty on the other hand, knows the contents and is forbidden from revealing the winner. Because the Deal or No Deal player is just as likely to open the winning box as a losing one, the Monty Hall advantage is lost. Assuming the grand prize is still left with two boxes remaining, the player has a 50/50 chance that the initially selected box contains the grand prize.

### Quantum version

A quantum version of the paradox illustrates some points about the relation between classical or non-quantum information and quantum information, as encoded in the states of quantum mechanical systems. The formulation is loosely based on Quantum game theory. The three doors are replaced by a quantum system allowing three alternatives; opening a door and looking behind it is translated as making a particular measurement. The rules can be stated in this language, and once again the choice for the player is to stick with the initial choice, or change to another "orthogonal" option. The latter strategy turns out to double the chances, just as in the classical case. However, if the show host has not randomized the position of the prize in a fully quantum mechanical way, the player can do even better, and can sometimes even win the prize with certainty (D'Ariano et al 2002).

### Similar problems

Despite similarity in their names, the game used in the Monty Hall problem is not related to three-card Monte.

## History of the problem

An essentially identical problem appeared as the Three Prisoners Problem in Martin Gardner's Mathematical Games column in Scientific American in 1959 (Gardner 1959). Gardner's version makes the selection procedure explicit, avoiding the unstated assumptions in the Parade version. This puzzle in probability theory involves three prisoners, a random one of whom has been secretly chosen to be executed in the morning. The first prisoner begs the guard to tell him which of the other two will go free, arguing that this reveals no information about whether the prisoner will be the victim; the guard responds by claiming that if the prisoner knows that a specific one of the other two prisoners will go free it will raise the first prisoner's subjective chance of being executed from 1/3 to 1/2. The question is whether the analysis of the prisoner or the guard is correct. In the version given by Martin Gardner, the guard then performs a particular randomizing procedure for selecting which name to give the prisoner; this gives the equivalent of the Monty Hall problem without the usual ambiguities in its presentation.

In 1975, Steve Selvin wrote a pair of letters to the American Statistician (Selvin 1975a, Selvin 1975b) regarding the Monty Hall problem. The first presented the problem in a version close to its most popular form; the version presented in Parade 15 years later is a restatement of Selvin's version. The second letter appears to be the first use of the term "Monty Hall problem". The problem is actually an extrapolation from the game show. Monty Hall did open a wrong door to build excitement, but offered a known lesser prize — such as \$100 cash — rather than a choice to switch doors. As Monty Hall wrote to Selvin (Hall 1975):
And if you ever get on my show, the rules hold fast for you — no trading boxes after the selection.

Phillip Martin's article in a 1989 issue of Bridge Today magazine titled "The Monty Hall Trap" (Martin 1989) presented Selvin's problem, with the correct solution, as an example of how one can fall into the trap of treating non-random information as if it were random. Martin then gives examples in the game of bridge where players commonly miscalculate the odds by falling into the same trap, such as the Principle of Restricted Choice. Given the controversy that would arise over this problem a year later, Martin showed a remarkable lack of prescience when he stated, "Here [in the Monty Hall problem] the trap is easy to spot. But the trap can crop up more subtly in a bridge setting."

A restated version of Selvin's problem statement appeared in Marilyn vos Savant's Ask Marilyn question-and-answer column of Parade in September 1990 (vos Savant 1990). Though vos Savant gave the correct answer that switching would win two-thirds of the time, vos Savant estimates 10,000 readers including several hundred mathematics professors wrote in to declare that her solution was wrong. As a result of the publicity the problem earned the alternative name Marilyn and the Goats.

The Parade column and its response received considerable attention in the press, including a front page story in the New York Times (Tierney 1991) in which Monty Hall himself was interviewed. He appeared to understand the problem quite well, giving the reporter a demo with car keys and explaining how actual game play on Let's Make a Deal differed from the rules of the puzzle.

The problem continues to resurface outside of academia. The syndicated NPR program, Car Talk, featured it as one of their weekly "Puzzlers," and the answer they featured was quite clearly explained as the correct one (Magliozzi & Magliozzi, 1998). An account of mathematician Paul Erdős's first encounter of the problem can be found in The Man Who Loved Only Numbers — like many others, he initially got it wrong. The problem is discussed, from the perspective of a boy with Asperger syndrome, in The Curious Incident of the Dog in the Night-time, a 2003 novel by Mark Haddon. The problem is also addressed in a lecture by the character Charlie Eppes in an episode of the CBS drama NUMB3RS (Episode 1.13) and in Derren Brown's 2006 book Tricks Of The Mind.

## References

• Adams, Cecil (1990)."On 'Let's Make a Deal,' you pick Door #1. Monty opens Door #2—no prize. Do you stay with Door #1 or switch to #3?", The Straight Dope, (November 2 1990). Retrieved July 25, 2005.
• Bapeswara Rao, V. V. and Rao, M. Bhaskara (1992). "A three-door game show and some of its variants". The Mathematical Scientist 17, no. 2, pp. 89–94
• Bohl, Alan H.; Liberatore, Matthew J.; and Nydick, Robert L. (1995). "A Tale of Two Goats … and a Car, or The Importance of Assumptions in Problem Solutions". Journal of Recreational Mathematics 1995, pp. 1–9.
• D'Ariano, G.M et al (2002). "The Quantum Monty Hall Problem" (PDF). Los Alamos National Laboratory, (February 21, 2002). Retrieved January 15, 2007.
• Gardner, Martin (1959). "Mathematical Games" column, Scientific American, October 1959, pp. 180–182. Reprinted in The Second Scientific American Book of Mathematical Puzzles and Diversions.
• Gill, Jeff (2002). Bayesian Methods, pp. 8–10. CRC Press. ISBN 1-5848-8288-3.
• Hall, Monty (1975). The Monty Hall Problem. LetsMakeADeal.com. Includes May 12, 1975 letter to Steve Selvin. Retrieved January 15, 2007.
• Magliozzi, Tom; Magliozzi, Ray (1998). Haircut in Horse Town: & Other Great Car Talk Puzzlers. Diane Pub Co.. ISBN 0-7567-6423-8.
• Martin, Phillip (1989). "The Monty Hall Trap", Bridge Today, May–June 1989. Reprinted in Granovetter, Pamela and Matthew, ed. (1993), For Experts Only, Granovetter Books.
• Mueser, Peter R. and Granberg, Donald (May 1999). "The Monty Hall Dilemma Revisited: Understanding the Interaction of Problem Definition and Decision Making", University of Missouri Working Paper 99-06. Retrieved July 5, 2005.
• Nahin, Paul J. (2000). Duelling idiots and other probability puzzlers. Princeton University Press, Princeton, NJ, pp. 192–193. ISBN 0-691-00979-1.
• Pearl, Judea (1988). Probabilistic Reasoning in Intelligent Systems, Morgan Kaufmann.
• Pearl, Judea (1990). Reasoning with Belief Functions: An Analysis of Compatibility, The International Journal of Approximate Reasoning, 4(5/6):363-389.
• Pearl, Judea (1992). Rejoinder to Comments on `Reasoning with Belief Functions: An Analysis of Compatibility', The International Journal of Approximate Reasoning, 6(3):425-443.
• Selvin, Steve (1975a). "A problem in probability" (letter to the editor). American Statistician 29(1):67 (February 1975).
• Selvin, Steve (1975b). "On the Monty Hall problem" (letter to the editor). American Statistician 29(3):134 (August 1975).
• Tierney, John (1991). "Behind Monty Hall's Doors: Puzzle, Debate and Answer?", The New York Times (21 July 1991), Sunday, Section 1; Part 1; Page 1; Column 5
• vos Savant, Marilyn (1990). "Ask Marilyn" column, Parade Magazine p. 12 (17 February 1990). [cited in Bohl et al., 1995]
• vos Savant, Marilyn (2006). "Ask Marilyn" column, Parade Magazine p. 6 (26 November 2006)

Probability is the likelihood that something is the case or will happen. Probability theory is used extensively in areas such as statistics, mathematics, science and philosophy to draw conclusions about the likelihood of potential events and the underlying mechanics of
Hosts:
Monty Hall (1963-1977, 1980-1981, 1984-1986, 1990-1991)
Bob Hilton (1990)
Billy Bush (2003)
Assistant:
Carol Merrill (1963-1977)
Announcers:
Wendell Niles (1963-1964)
Jay Stewart (1964-1977)
Maurice "Monty Hall" Halperin, O.C., B.Sc., LL.D (born August 25 1921 in Winnipeg, Manitoba) is a Canadian-born actor, singer and sportscaster, best known as the host of the long-running television game show Let's Make a Deal.
Marilyn vos Savant
Born August 11, 1946
St. Louis, MO
Nationality American
Field Writer
Alma mater Washington University
Known for "Highest IQ"
PARADE is a magazine, distributed as a Sunday supplement in hundreds of newspapers in the United States. It was founded in 1941 and is owned by Advance Publications.
In mathematics, computing, linguistics, and related disciplines, an algorithm is a finite list of well-defined instructions for accomplishing some task that, given an initial state, will proceed through a well-defined series of successive states, eventually terminating in an
Venn diagrams are illustrations used in the branch of mathematics known as set theory. They show all of the possible mathematical or logical relationships between sets (groups of things).
In operations research, specifically in decision analysis, a decision tree (or tree diagram) is a decision support tool that uses a graph or model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility.
Bayesian probability is an interpretation of the probability calculus which holds that the concept of probability can be defined as the degree to which a person (or community) believes that a proposition is true.
A prior probability is a marginal probability, interpreted as a description of what is known about a variable in the absence of some evidence. The posterior probability is then the conditional probability of the variable taking the evidence into account.
Conditional probability is the probability of some event A, given the occurrence of some other event B. Conditional probability is written P(A|B), and is read "the probability of A, given B".
The posterior probability of a random event or an uncertain proposition is the conditional probability that is assigned when the relevant evidence is taken into account.
Bayes' theorem (also known as Bayes' rule or Bayes' law) is a result in probability theory, which relates the conditional and marginal probability distributions of random variables.
The concept of a normalizing constant arises in probability theory and a variety of other areas of mathematics.

## Definition and examples

In probability theory, a normalizing constant
Conditional probability is the probability of some event A, given the occurrence of some other event B. Conditional probability is written P(A|B), and is read "the probability of A, given B".
playing card is a typically hand-sized piece of heavy paper or thin plastic. A complete set of cards is a pack or deck. A deck of cards is used for playing one of many card games, some of which constitute gambling.
The law of large numbers (LLN) is a theorem in probability that describes the long-term stability of a random variable. Given a sample of independent and identically distributed random variables with a finite population mean and variance, the average of these observations will
Deal or No Deal is the name of several closely related television game shows, the first of which (launching the format) was produced by Dutch producer Endemol.

## Format

Deal or No Deal
In quantum mechanics, quantum information is physical information that is held in the "state" of a quantum system. The most popular unit of quantum information is the qubit, a two-state quantum system.
Quantum game theory, concisely put, is an extension of classical game theory to the quantum domain. It differs from classical game theory in three primary ways:
1. Superposed initial states,
2. Quantum Entanglement of initial states,

Three Cards is a simple but slightly counterintuitive puzzle used as a standard example in probability theory. Its solution illustrates some basic principles, including the Kolmogorov axioms.
The Boy or Girl problem is a well-known example in probability theory:
• A random two-child family whose older child is a boy is chosen. What is the probability that the younger child is a girl?
• A random two-child family with at least one boy is chosen.

The Three Prisoners Problem appeared in Martin Gardner's Mathematical Games column in 1959. It is completely analogous to the Monty Hall problem.

There are three prisoners, A, B, and C. Two of them will be released and one will be executed.
The Three Prisoners Problem appeared in Martin Gardner's Mathematical Games column in 1959. It is completely analogous to the Monty Hall problem.

There are three prisoners, A, B, and C. Two of them will be released and one will be executed.