06-21-2026, 01:10 PM
Probabilistic Programming
Summary
The Cornell CS 4110 lecture on Probabilistic Programming explains how ideas from programming languages can be applied to statistical modeling and machine learning. Instead of writing traditional software that simply uses randomness, probabilistic programming languages (PPLs) allow programmers to describe probabilistic models using familiar programming abstractions such as functions, variables, and control flow, while tools automatically perform statistical inference.
The lecture introduces concepts such as random primitives, conditioning, Bayesian networks, latent variables, and inference methods like enumeration, rejection sampling, and Markov chain Monte Carlo (MCMC). Through examples like recommending research papers based on student interests, it shows how PPLs simplify the creation and analysis of complex models by shifting much of the mathematical and computational burden to automated tools. The broader goal is to make machine learning and statistical reasoning more accessible by combining the power of programming languages with probabilistic modeling techniques.
ARTICLE
Summary
The Cornell CS 4110 lecture on Probabilistic Programming explains how ideas from programming languages can be applied to statistical modeling and machine learning. Instead of writing traditional software that simply uses randomness, probabilistic programming languages (PPLs) allow programmers to describe probabilistic models using familiar programming abstractions such as functions, variables, and control flow, while tools automatically perform statistical inference.
The lecture introduces concepts such as random primitives, conditioning, Bayesian networks, latent variables, and inference methods like enumeration, rejection sampling, and Markov chain Monte Carlo (MCMC). Through examples like recommending research papers based on student interests, it shows how PPLs simplify the creation and analysis of complex models by shifting much of the mathematical and computational burden to automated tools. The broader goal is to make machine learning and statistical reasoning more accessible by combining the power of programming languages with probabilistic modeling techniques.
ARTICLE
┌────────────────────────────────┐
│ KONSTANTINOS MICHAILIDIS │
└────────────────────────────────┘
│ KONSTANTINOS MICHAILIDIS │
└────────────────────────────────┘