Formalized Probability Theory and Applications Using Theorem by Osman Hasan, Sofiène Tahar

By Osman Hasan, Sofiène Tahar

Scientists and engineers usually need to care for structures that express random or unpredictable parts and needs to successfully evaluation possibilities in every one scenario. desktop simulations, whereas the conventional software used to resolve such difficulties, are restricted within the scale and complexity of the issues they could solve.

Formalized likelihood thought and functions utilizing Theorem Proving discusses a few of the barriers inherent in computers whilst utilized to difficulties of probabilistic research, and offers a unique option to those obstacles, combining higher-order good judgment with computer-based theorem proving. Combining useful program with theoretical dialogue, this booklet is a vital reference software for mathematicians, scientists, engineers, and researchers in all STEM fields.

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2013). Information-theoretic analysis using theorem proving. (PhD thesis). Concordia University, Montreal, Canada. , & Tahar, S. (2010). On the formalization of the Lebesgue integration theory in HOL. In I. T. ), LNCS (Vol. 6172, pp. 387–402). Springer. , & Tahar, S. (2011). Formalization of entropy measures in HOL. In I. T. ), LNCS (Vol. 6898, pp. 233–248). Springer. KEY TERMS AND DEFINITIONS Extended Real Numbers: Real numbers including ±∞ are usually referred to as the extended real numbers.

Quite frequently, along with the average value, we are also interested in finding how typical is the average value or in other words the chances of observing an event far from the average. One possible way to measure the variation, or spread, of these values is to consider the quantity Ex[|X − Ex[X]|], where | y | denote the abs value of y. However, it turns out to be mathematically inconvenient to deal with this quantity, so a more tractable quantity called variance is usually considered, which returns the expectation of the square of the difference between R and its expectation.

Due to the formal nature of the models and properties and the inherent soundness of the theorem proving approach, probabilistic analysis carried out in this way will be free from any approximation and precision issues. Similarly, the high expressiveness of higher-order logic allows us to analyze a wider range of 17 Formal Verification Methods systems without any modeling limitations, such as the state-space explosion problem in the case of probabilistic model checking, and formally verify analytically complex properties, such as expectation, variance and tail distribution bounds.

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