Learn what probabilistic programming is, how it works, and what are some of the best ways to use it for natural language processing tasks.

In a probabilistic programming language, the heavy lifting is done by the inference algorithm -- the algorithm that continuously readjusts probabilities on the basis of new pieces of training data.

In what promises to be a huge game changer, computer scientists have developed so-called probabilistic programming languages, which let researchers mix and match different machine-learning techniques.

The field of Probabilistic Logic Programming (PLP) has seen significant advances in the last 20 years, with many proposals for languages that combine probability with logic programming. Since the ...

Probabilistic programming is a paradigm or technique that combines programming tools with bayesian statistical simulation, inference methods, and machine learning components. You may argue that a deep ...

As discussed later, probabilistic programming offers an elegant way of generalizing graphical models, allowing a much richer representation of models.

Probabilistic logic programming extends logic programming by enabling the representation of uncertain information by means of probability theory. Probabilistic logic programming is at the intersection ...

Probabilistic algorithms for doing Markov chain, Monte Carlo and variational inferencing; and End-to-end examples with scripts and tutorial notebooks for programming in TensorFlow probability.

Probabilistic programming languages (PPLs) have emerged as a transformative tool for expressing complex statistical models and automating inference procedures.