Data Science / Bayesian Statistics / R Foundation / R / Statistical analysis using Bayesian Networks

R - Statistical analysis using Bayesian Networks

Details

ID 2859212
Classroom 2 days
Webinar 4 days
Method Presentation with examples and hands-on labs.
Prequisite Basics in Statistics
Audience Data Analysts

Services:

  • Lunch / Catering
  • Assistance for hotel / travel bookings
  • Comelio certificate
  • Flexible: Free cancellation up until 10 days before the training

Summary

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.

Training Dates

  • 2020-Nov-02 - Nov-05
  • 2021-Jan-11 - Jan-14
  • 2021-Mar-22 - Mar-25
  • 2021-May-31 - Jun-03

750 EUR +VAT

Location | Enrollment


Agenda

Introduction

Introduction to Graph Theory: Graphs, Nodes, and Arcs - Bayesian Networks

Bayesian Networks and Static Data

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

Bayesian Networks and Time Series Data

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

Bayesian Network Inference Algorithms

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

Trainer

Marco Skulschus (born in Germany in 1978) studied economics in Wuppertal (Germany) and Paris (France) and wrote his master´s thesis about semantic data modeling. He started working as a lecturer and consultant in 2002.

Publications
  • Grundlagen empirische Sozialforschung ISBN 978-3-939701-23-1
  • System und Systematik von Fragebögen ISBN 978-3-939701-26-2
  • Oracle PL/SQL ISBN 978-3-939701-40-8
  • MS SQL Server - T-SQL Programmierung und Abfragen ISBN 978-3-939701-69-9
Projects

- He works as an IT-consultant and project manager. He developed various Business Intelligence systems for industry clients and the public sector. For several years now, he is responsible for a BI-team in India which is mainly involved in BI and OLAP projects, reporting systems as well as statistical analysis and Data Mining.

Research

He led several research projects and was leading scientist and project manager of a publicly funded project about interactive questionnaires and online surveys.