This article explains the principles of probabilistic programming languages and walks through a step‑by‑step implementation of a minimal PPL in Python, covering model definition, variable representation, DAG traversal, log‑density computation, and a posterior grid illustration.
This article explains the principles of probabilistic programming languages and walks through constructing a basic PPL in Python, covering model definition with latent and observed variables, distribution handling, DAG traversal for log‑density computation, and demonstrates evaluation with example code and visualizations.
This article explains Bayesian statistics as a probabilistic framework, describes how modern numerical methods and probabilistic programming languages automate inference, and reviews both Markov and non‑Markov techniques such as MCMC, grid computation, Laplace approximation, and variational inference for building complex models.
MarkovJunior, a probabilistic programming language based on Markov algorithms, can automatically create countless non‑repeating 2D and 3D mazes, terrain maps, and circuit diagrams for games, using simple rewrite rules and a single line of code.
This article explains how probabilistic programming languages work and walks through building a minimal PPL in Python, covering model definition, latent and observed variables, DAG traversal, log‑density computation, and extensions such as posterior grid approximation.
