Probabilistic-Graphical-Model
Probabilistic Graphical Models Specialization from Stanford University
Probabilistic graphical models (PGMs) are a rich framework for encoding probability distributions over complex domains: joint (multivariate) distributions over large numbers of random variables that interact with each other. These representations sit at the intersection of statistics and computer science, relying on concepts from probability theory, graph algorithms, machine learning, and more. They are the basis for the state-of-the-art methods in a wide variety of applications, such as medical diagnosis, image understanding, speech recognition, natural language processing, and many, many more. They are also a foundational tool in formulating many machine learning problems.
- Bayesian Networks
- Markov Networks
- Gibbs Sampling
- Decision Theory
- Belief Propogation Algorithms
- MAP algorithms
- Makov chain monte carlo sampling
- Metropolis Hastings Sampling
- Conditional Random Fields
- Probabilistic Inference
- Factor Graphs
- Expectation Maximization Algorithm