Download An Introduction to Probabilistic Programming pdf by Jan-Willem van de Meent, This document is intended to be a first-year graduate-level introduction to probabilistic programming. It not solely provides a thorough background for anyone wishing to use a probabilistic programming system, however conjointly introduces the techniques required to design and build these systems. it's aimed at people who have an undergraduate-level understanding of either or, ideally, each probabilistic machine learning and programming languages. Download the pdf from below to explore all topics and start learning.


1 Introduction
1.1 Model-based Reasoning
1.2 Probabilistic Programming
1.3 Example Applications
1.4 A First Probabilistic Program
2 A Probabilistic Programming Language Without Recursion
2.1 Syntax
2.2 Syntactic Sugar
2.3 Examples
2.4 A Simple Purely Deterministic Language
3 Graph-Based Inference
3.1 Compilation to a Graphical Model
3.2 Evaluating the Density
3.3 Gibbs Sampling
3.4 Hamiltonian Monte Carlo
3.5 Compilation to a Factor Graph
3.6 Expectation Propagation
4 Evaluation-Based Inference I
4.1 Likelihood Weighting
4.2 Metropolis-Hastings
4.3 Sequential Monte Carlo
4.4 Black Box Variational Inference
5 A Probabilistic Programming Language With Recursion
5.1 Syntax
5.2 Syntactic sugar
5.3 Examples
6 Evaluation-Based Inference II
6.1 Explicit separation of model and inference code
6.2 Addressing Transformation
6.3 Continuation-Passing-Style Transformation
6.4 Message Interface Implementation
6.5 Likelihood Weighting
6.6 Metropolis-Hastings
6.7 Sequential Monte Carlo
7 Advanced Topics
7.1 Inference Compilation
7.2 Model Learning
7.3 Hamiltonian Monte Carlo and Variational Inference
7.4 Nesting
7.5 Formal Semantics
8 Conclusion