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Monty-Hall (classical-host) Theorem and Monty-Hall (strategist-host) Theorem

Authors: Halemane, Keshava Prasad;

Monty-Hall (classical-host) Theorem and Monty-Hall (strategist-host) Theorem

Abstract

Monty-Hall (classical-host) Theorem and Monty-Hall (strategist-host) Theorem Dr.(Prof.) Keshava Prasad Halemane, Professor - retired from Department of Mathematical And Computational Sciences, National Institute of Technology Karnataka Surathkal, Srinivasnagar, Mangaluru - 575025, India. https://orcid.org/0000-0003-3483-3521 Independent Researcher (no funding) SASHESHA, 8-129/12 Sowjanya Road, Naigara Hills, Bikarnakatte, Kulshekar Post, Mangaluru-575005. Karnataka State, India https://www.linkedin.com/in/keshavaprasadahalemane/ k.prasad.h@gmail.com ABSTRACT The Monty-Hall (classical-host) Theorem is presented along with a constructive proof by solving the classical Monty-Hall Problem. It establishes the fact that the probability of winning the prize is unaffected by a switched-choice; unlike the most prevalent and widely accepted position held by the leading subject matter experts. A parameterized supermodel is presented, with the associated generic Monty-Hall (strategist-host) Theorem, along with a constructive proof, by solving the corresponding Monty-Hall Problem, wherein the host plays a generic parameterized strategy on the guest. This model subsumes the Monty-Hall (classical) Problem. It establishes the limits on the range of values for the probability of winning the prize, with or without a possible switched-choice. Eight extreme strategies have been identified and characterized. It is established that there does not exist any strategy, that a strategist-host may play on the guest, which would result in a situation wherein a switched-choice will always (irrespective of the placement of the prize and irrespective of the initial-choice of the guest) lead to an enhancement in the chances of winning the prize for the guest. The clearly partitioned three-dimensional discrete event(sample)space, with the twelve mutually-exclusive together-exhaustive possible alternatives, along with the corresponding apriori probabilities, presented as the input data set, is a fail-safe framework to study, analyze & solve the problem; with no possibility of missing any relevant component terms or including any irrelevant component terms, while going through the required calculations in order to derive the desired results. Keywords: Monty-Hall (classical-host) Theorem; Monty-Hall (strategist-host) Theorem; Monty-Hall Theorems; Bayes-Price Rule; Bayes Theorem; Discrete Event (Sample) Space; Parameterized Strategy; Perturbation Parameters; AMS MSC Mathematics Subject Classification: 60A99; 60C99; 62A99; 62C99.

Keywords

Monty-Hall Problem, Monty-Hall Theorem, A-Priori Probability, Monty-Hall (classical-host) Theorem, Mutually Exclusive Together Exhaustive Alternatives, Bayes Theorem, Conditional Probability, Discrete Event (Sample) Space, Bayes-Price Rule, Mutually Independent Events, Parameterized Strategy, Joint Probability, Marginal Probability, Monty-Hall (strategist-host) Theorem, Monty-Hall Theorems, A-Posteriori Probability, Restricted Probability, Perturbation Parameters

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
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