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 and Inference Algorithms Publication Trend The graph below shows the total number of publications each year in Probabilistic Programming and Inference Algorithms.

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 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.

"Probabilistic" programming can instruct a computer using just 50 lines of code to complete a task that used to take thousands of lines, researchers say. New programming languages are being ...

The first step in using GenSQL is to create a probabilistic model of their tabular data, using a “probabilistic program synthesis tool,” such as CrossCat. Once a user’s data has been turned into a ...

Ron Shamir, Probabilistic Analysis in Linear Programming, Statistical Science, Vol. 8, No. 1, Report from the Committee on Applied and Theoretical Statistics of the National Research Council on ...

Deep probabilistic programming is a new development in machine learning that combines the principled treatment of uncertainty provided by Bayesian statistics with the capabilities of deep learning.

What are some advanced concepts in programming that most average programmers have never heard of? This question was originally answered on Quora by Tikhon Jelvis.