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training duration icon R - Statistical analysis using Bayesian Networks

Duration: 2 Days
Delivery Type: Classroom
Target Audience: Data Analysts
Course Number: 1000019
Method: Presentation with examples and hands-on labs.
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A. Introduction

[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

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For trainings with hands-on labs you are kindly requested to bring your own laptop with the required software. Alternatively, you can use our virtual machines (VMWare) for the MS Windows OS.
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