Probabilistic programming best universities in uk

The probabilistic-programming mailing list hosted at CSAIL/MIT hopes to support discussion between researchers working in the area of probabilistic programming, but also to provide a means to announce new results, software, workshops, etc. U america the mailing list is fashioned after the popular "uai" mailing list.

Probabilistic graphical models provide a formal lingua franca for modeling and a common target for efficient inference algorithms. Their introduction gave rise to an extensive body of work in machine learning, statistics, robotics, vision, biology, neuroscience, artificial intelligence (AI) and cognitive science. However, many of the most innovative and useful probabilistic models published by the AI, machine learning, and statistics community far outstrip the representational capacity of graphical models and associated inference techniques.

Models are communicated using a mix of natural language, pseudo code, and mathematical formulae and solved using special purpose, one-off inference methods. Rather than precise specifications suitable for automatic inference, graphical models typically serve as coarse, high-level descriptions, eliding critical aspects such as fine-grained independence, abstraction and recursion.

PROBABILISTIC PROGRAMMING LANGUAGES aim to close this representational gap, unifying general purpose programming with probabilistic modeling; literally, users specify a probabilistic model in its entirety (e.G., by writing code that generates a sample from the joint distribution) and inference follows automatically given the specification. These languages provide the full power of modern programming languages for describing complex distributions, and can enable reuse of libraries of models, support interactive modeling and formal verification, and provide a much-needed abstraction barrier to foster generic, efficient inference in universal model classes.

We believe that the probabilistic programming language approach within AI has the potential to fundamentally change the way we understand, design, build, test and deploy probabilistic systems. This approach has seen growing interest within AI over the last 10 years, yet the endeavor builds on over 40 years of work in range of diverse fields including mathematical logic, theoretical computer science, formal methods, programming languages, as well as machine learning, computational statistics, systems biology, probabilistic AI.

A growing body of literature studies probabilistic programming from an array of perspectives. The individual project pages linked below often contain lists of publications, although we aim to collect these in our own master list as well. A related but distinct body of work is that of approximate bayesian computation (ABC), which focuses on likelihood-free methods, developed originally to tackle statistical queries in population genetics but now applied more broadly. The website for the i-like research programme links to a number of very interesting articles. Washington university st louis tuition another related area of research is statistical relational learning, which is in general interested in distributions on structured spaces (e.G., models of first order languages) where there may be uncertainty in the number and types of objects.

Below we have compiled a list of probabilistic programming systems including languages, implementations/compilers, as well as software libraries for constructing probabilistic models and toolkits for building probabilistic inference algorithms.

• BLOG, or bayesian logic, is a probabilistic programming language with elements of first-order logic, as well as an MCMC-based inference algorithm. BLOG makes it relatively easy to represent uncertainty about the number of underlying objects explaining observed data.

• BUGS is a language for specifying finite graphical models and accompanying software for performing B(ayesian) I(nference) U(sing) G(ibbs) S(ampling), although modern implementations (such as winbugs, JAGS, and openbugs) are based on metropolis-hastings. Top universities uk biips is an implementation based on interacting particle systems methods like sequential monte carlo.

• church is a universal probabilistic programming language, extending scheme with probabilistic semantics, and is well suited for describing infinite-dimensional stochastic processes and other recursively-defined generative processes (goodman, mansinghka, roy, bonawitz and tenenbaum, 2008). The active implementation of church is webchurch. Older implementations include MIT-church, cosh, bher, and jschurch. See also venture below.

• HANSEI is a domain-specific language embedded in ocaml, which allows one to express discrete-distribution models with potentially infinite support, perform exact inference as well as importance sampling-based inference, and model inference over inference.

• NIMBLE is a system for building and sharing analysis methods for statistical models, especially for hierarchical models and computationally-intensive methods. NIMBLE is built in R but compiles your models and algorithms using C++ for speed. NIMBLE borrows the syntax of BUGS.

• praise is a system performing probabilistic inference without grounding random variables or sampling, but instead performing it exactly and directly over an expressive higher-level language involving difference arithmetic over integers, linear real arithmetic, equality over categorical types, with relational random variables and algebraic data types coming next.

• pymc is a python module that implements a suite of MCMC algorithms as python classes, and is extremely flexible and applicable to a large suite of problems. List of best universities in the world pymc includes methods for summarizing output, plotting, goodness-of-fit and convergence diagnostics.

• venture is an interactive, turing-complete, higher-order probabilistic programming platform that aims to be sufficiently expressive, extensible and efficient for general-purpose use. Its virtual machine supports multiple scalable, reprogrammable inference strategies, plus two front-end languages: venchurch and venturescript.

Freer, roy, and tenenbaum relate turing’s legacy to probabilistic programming approaches in artificial intelligence in this book chapter, appearing in a volume edited by rod downey, entitled turing’s legacy and being published by cambridge university press in their ASL lecture notes in logic series.

This book provides an introduction to probabilistic programming focusing on practical examples and applications. No prior experience in machine learning or probabilistic reasoning is required. The book uses figaro to present the examples but the principles are applicable to many probabilistic programming systems.