Examples of probabilistic models

Examples of probabilistic models include Bayesian networks for causal reasoning, Hidden Markov Models for sequential data analysis, Gaussian Mixture Models for clustering, and Naive Bayes classifiers for text classification. These models represent uncertainty through probability distributions rather than deterministic outputs. Common implementations encompass Gaussian processes for regression with uncertainty quantification, Markov Chain Monte Carlo methods for complex inference, variational autoencoders for generative modeling, and probabilistic neural networks that output confidence estimates. Reinforcement learning employs probabilistic policies for exploration-exploitation balance, while natural language processing uses n-gram models and topic models like Latent Dirichlet Allocation. Computer vision applications include probabilistic graphical models for image segmentation and Kalman filters for object tracking. These models enable robust decision-making under uncertainty, confidence estimation, and principled handling of noisy data. For AI agents, probabilistic models provide frameworks for risk assessment, belief updating, and handling uncertain environments.