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

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.

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.

"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 ...

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.

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 ...

Probabilistic programming does in 50 lines of code what used to take thousands Most recent advances in artificial intelligence—such as mobile apps that convert speech to text—are the result of ...

This note deals with the manner in which dynamic problems, involving probabilistic constraints, may be tackled using the ideas of Lagrange multipliers and efficient solutions. Both the infinite and ...

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.