Definition of Probabilistic Programming
Probabilistic Programming Languages (PPLs) enable the definition of generative models as code. PPL compilers automate the process of Bayesian Inference, determining parameters that best represent observed data through sampling or variational optimization.
Core Frameworks
- Stan: A platform for high-performance statistical modeling utilizing Hamiltonian Monte Carlo (HMC) sampling.
- Pyro (Uber): A deep probabilistic programming language built on PyTorch, supporting large-scale variational inference.
- Edward (GitHub): A library for probabilistic modeling and statistical criticism.
Analysis
PPLs facilitate the modeling of uncertainty in autonomous agents. By representing agent beliefs as probability distributions rather than point estimates, it is possible to perform risk-sensitive inference and belief updating based on evidence streams.