[Duration: 0.25 Days] Introduction to Graph Theory: Graphs, Nodes, and Arcs - Bayesian Networks
B. Bayesian Networks and Static Data
[Duration: 0.75 Days] Bayesian Networks: Essential Definitions and Properties: Graph Structure and Probability Factorization, Fundamental Connections, Equivalent Structures, Markov Blankets - Static Bayesian Networks Modeling: Constraint-Based Structure Learning Algorithms, Score-Based Structure Learning Algorithms, Hybrid Structure Learning Algorithms, Parameter Learning
C. Bayesian Networks and Time Series Data
[Duration: 0.5 Days] Time Series and Vector Auto-Regressive Processes (VAR) - Dynamic Bayesian Networks: Essential Definitions and Properties, Dynamic Bayesian Network Representation of a VAR Process - Algorithms: Least Absolute Shrinkage and Selection Operator (LASSO), James–Stein Shrinkage, First-Order Conditional Dependencies Approximation
D. Bayesian Network Inference Algorithms
[Duration: 0.25 Days] Reasoning Under Uncertainty: Probabilistic Reasoning and Evidence, Algorithms for Belief Updating: Exact and Approximate Inference, Causal Inference - Inference in Static Bayesian Networks: Exact Inference, Approximate Inference - Inference in Dynamic Bayesian Networks
A Bayesian network, Bayes network, belief network, Bayes(ian) model or probabilistic directed acyclic graphical model is a probabilistic graphical model that represents a set of random variables and their conditional dependencies via a directed acyclic graph. There are three main inference tasks for Bayesian networks: Structure learning, inferring unobserved variables, and parameter learning. This training presents the diverse techniques of statistical data analysis using Bayesian networks and shows in hands-on labs using R how to implement the techniques and algorithms. You will become familiar with R packages like bnlearn, deal, pcalg, and catnet for structure learning, and you will get to know packages like gRbase and gRain for inferential analysis. Time series data will be analyzed using packages like vars, lars, simone, and GeneNet.