Verification of Probabilistic Properties in HOL using the Cumulative Distribution Function

Abstract. Traditionally, computer simulation techniques are used to perform probabilistic analysis. However, they provide inaccurate results and cannot handle large-scale problems due to their enormous CPU time requirements. To overcome these limitations, we propose to complement simulation based tools with higher-order-logic theorem proving so that an integrated approach can provide exact results for the critical sections of the analysis in the most efficient manner. In this paper, we illustrate the practical effectiveness of our idea by verifying numerous probabilistic properties associated with random variables in the HOL theorem prover. Our verification approach revolves around the fact that any probabilistic property associated with a random variable can be verified using the classical Cumulative Distribution Function (CDF) properties, if the CDF relation of that random variable is known. For illustration purposes, we also present the verification of a couple of probabilistic properties, which cannot be evaluated precisely by the existing simulation techniques, associated with the Continuous Uniform random variable in HOL

Towards the Formal Reliability Analysis of Oil and Gas Pipelines

It is customary to assess the reliability of underground oil and gas pipelines in the presence of excessive loading and corrosion effects to ensure a leak-free transport of hazardous materials. The main idea behind this reliability analysis is to model the given pipeline system as a Reliability Block Diagram (RBD) of segments such that the reliability of an individual pipeline segment can be represented by a random variable. Traditionally, computer simulation is used to perform this reliability analysis but it provides approximate results and requires an enormous amount of CPU time for attaining reasonable estimates. Due to its approximate nature, simulation is not very suitable for analyzing safety-critical systems like oil and gas pipelines, where even minor analysis flaws may result in catastrophic consequences. As an accurate alternative, we propose to use a higher-order-logic theorem prover (HOL) for the reliability analysis of pipelines. As a first step towards this idea, this paper provides a higher-order-logic formalization of reliability and the series RBD using the HOL theorem prover. For illustration, we present the formal analysis of a simple pipeline that can be modeled as a series RBD of segments with exponentially distributed failure times.Comment: 15 page

Formal Reliability Analysis using Higher-order Logic Theorem Proving

Traditional techniques used in the reliability analysis of engineering systems have limitations. Paper-and-pencil based analysis is prone to human error and simulation based techniques cannot be computationally one hundred percent accurate. An alternative to these two traditional approaches is modeling and analysis of reliability of systems using formal methods based techniques such as probabilistic theorem proving. Probabilistic theorem proving using higher-order logic can be used for modeling and analysis of reliability of engineering systems provided a certain reasoning infrastructure is developed. The developed infrastructure can include random variables, their probabilistic and statistical properties, and basic reliability theory concepts such as survival and hazard functions. This thesis describes state-of-the-art research in reliability analysis using theorem proving. It also describes the main contributions of this thesis which include: the formalization of statistical properties of continuous random variables, the formalization of multiple continuous random variables and the formalization of the basic notions of reliability that can be applied to single and multiple component systems. Engineering applications of the formalization are presented that illustrate the usefulness of our formalization infrastructure. These applications include reliability analysis of electronic system components such as a capacitor and an underground power transmission cable. We also present the reliability analysis of an automobile transmission using our higher-order logic formalization. To the best of our knowledge, for the very first time, the use of theorem proving based infrastructure enables formal reliability analysis of engineering systems that is computationally one hundred percent accurate and sound. The analysis is performed using real and true random variables. We show that the results presented in this thesis are general and can be applied to many reliability engineering problems

Using Theorem Proving to Verify Expectation and Variance for Discrete Random Variables

Statistical quantities, such as expectation (mean) and variance, play a vital role in the present age probabilistic analysis. In this paper, we present some formalization of expectation theory that can be used to verify the expectation and variance characteristics of discrete random variables within the HOL theorem prover. The motivation behind this is the ability to perform error free probabilistic analysis, which in turn can be very useful for the performance and reliability analysis of systems used in safety-critical domains, such as space travel, medicine and military. We first present a formal definition of expectation of a function of a discrete random variable. Building upon this definition, we formalize the mathematical concept of variance and verify some classical properties of expectation and variance in HOL. We then utilize these formal definitions to verify the expectation and variance characteristics of the Geometric random variable. In order to demonstrate the practical effectiveness of the formalization presented in this paper, we also present the probabilistic analysis of the Coupon Collector’s problem in HOL

Formal verification of tail distribution bounds in the HOL theorem prover

Tail distribution bounds play a major role in the estimation of failure probabilities in performance and reliability analysis of systems. They are usually estimated using Markov's and Chebyshev's inequalities, which represent tail distribution bounds for a random variable in terms of its mean or variance. This paper presents the formal verification of Markov's and Chebyshev's inequalities for discrete random variables using a higher-order-logic theorem prover. The paper also provides the formal verification of mean and variance relations for some of the widely used discrete random variables, such as Uniform(m), Bernoulli(p), Geometric(p) and Binomial(m, p) random variables. This infrastructure allows us to precisely reason about the tail distribution properties and thus turns out to be quite useful for the analysis of systems used in safety-critical domains, such as space, medicine or transportation. For illustration purposes, we present the performance analysis of the coupon collector's problem, a well-known commercially used algorithm