Verification of Expectation Properties for Discrete Random Variables in HOL

One of the most important concepts in probability theory is that of the expectation of a random variable, which basically summarizes the distribution of the random variable in a single number. In this paper, we develop the basic techniques for analyzing the expected values of discrete random variables in the HOL theorem prover. We first present a formalization of the expectation function for discrete random variables and based on this definition, the expectation properties of three commonly used discrete random variables are verified. Then, we utilize the definition of expectation in HOL to verify the linearity of expectation property, a useful characteristic to analyze the expected values of probabilistic systems involving multiple random variables. To demonstrate the usefulness of our approach, we verify the expected value of the Coupon Collector's problem within the HOL theorem prover.

[1]  Leon Sterling,et al.  Meta-Level Inference and Program Verification , 1982, CADE.

[2]  Józef Bia las The σ-additive Measure Theory , 1990 .

[3]  Mahesh Viswanathan,et al.  VESTA: A statistical model-checker and analyzer for probabilistic systems , 2005, Second International Conference on the Quantitative Evaluation of Systems (QEST'05).

[4]  David Williams,et al.  Probability with Martingales , 1991, Cambridge mathematical textbooks.

[5]  Christel Baier,et al.  Model-Checking Algorithms for Continuous-Time Markov Chains , 2002, IEEE Trans. Software Eng..

[6]  John N. Tsitsiklis,et al.  Introduction to Probability , 2002 .

[7]  Ramakant Khazanie Basic probability theory and applications , 1976 .

[8]  M. Mitzenmacher,et al.  Probability and Computing: Chernoff Bounds , 2005 .

[9]  Stephan Merz,et al.  Model Checking , 2000 .

[10]  Joe Hurd,et al.  Formal verification of probabilistic algorithms , 2003 .

[11]  Walter L. Smith Probability and Statistics , 1959, Nature.

[12]  Jan J. M. M. Rutten,et al.  Mathematical techniques for analyzing concurrent and probabilistic systems , 2004, CRM monograph series.

[13]  MA John Harrison PhD Theorem Proving with the Real Numbers , 1998, Distinguished Dissertations.

[14]  Sofiène Tahar,et al.  Verification of Probabilistic Properties in HOL Using the Cumulative Distribution Function , 2007, IFM.

[15]  Stefan Richter,et al.  Formalizing Integration Theory with an Application to Probabilistic Algorithms , 2004, TPHOLs.

[16]  Alan Bundy,et al.  Constructing Induction Rules for Deductive Synthesis Proofs , 2006, CLASE.

[17]  Charles M. Grinstead,et al.  Introduction to probability , 1999, Statistics for the Behavioural Sciences.

[18]  Andrzej Ne ' dzusiak Fields and Probability , 1990 .

[19]  Sheldon M. Ross Introduction to Probability Models. , 1995 .

[20]  Wenbo Mao,et al.  Modern Cryptography: Theory and Practice , 2003 .

[21]  Marta Z. Kwiatkowska,et al.  Quantitative Analysis With the Probabilistic Model Checker PRISM , 2006, QAPL.

[22]  Sofiène Tahar,et al.  Formalization of Continuous Probability Distributions , 2007, CADE.