Bayesian Statistical Probabilistic Programming

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Key Features

Bayesian programming is a formalism and methodology for specifying probabilistic models and solving problems when less than the required information is available.

Edwin T. Jaynes proposed that probability be viewed as an alternative and extension of logic for rational reasoning with incomplete and uncertain data.

The Logic of Science, which was not a physical device but an inference engine to automate probabilistic reasoning—a kind of Prolog for probability instead of logic.

Bayesian programming can be thought of as an algebraic formalism for specifying graphical models like Bayesian networks, dynamic Bayesian networks, and hidden Markov models.

Bayesian Programming is more general than Bayesian networks, with an expressive power comparable to probabilistic factor graphs.

Bayesian statistics gives you the tools to update your beliefs based on new data, which is useful in many real-world scenarios such as tracking pandemics, forecasting economic trends.

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    Most computer programs developed through Bayesian statistical probabilistic are highly equipped with generic inference algorithms to effectively answer these computer programs’ queries. By providing these algorithms, BSPP systems enable the programming students to focus on designing good models utilizing domain knowledge.
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    • Discrete probability theory
    • Basic measure theory and programs as measures
    • Sequential Monte Carlo and amortized inference
    • Variational inference
    • Markov chain Monte Carlo algorithms
    • Posterior inference
    • Generative modeling with Program Induction and 2-D Physics

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