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Bayesian Networks

This web-based training course on Bayesian Networks functionality, administration and development, is available online to all individuals, institutions, corporates and enterprises in India (New Delhi NCR, Bangalore, Chennai, Kolkatta), US, UK, Canada, Australia, Singapore, United Arab Emirates (UAE), China and South Africa. No matter where you are located, you can enroll for any training with us - because all our training sessions are delivered online by live instructors using interactive, intensive learning methods.

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Course Details

This course helps trainees to learn about Bayesian networks. The Bayesian is basically a type of statistical model that represents a set of random variables and their dependencies via a DAG (Directed Acyclic Graph). Generally, Bayesian networks are DAGs whose nodes represent random variables in the Bayesian sense: they might be observable quantities, unknown parameters or hypotheses. With the help of this training program trainees will learn probabilistic logic sampling methods, parameters and structural learning, Bayesian mixture models and applications in the different practical problems set up. This training program further helps to build various concepts for dynamic Bayesian networks, object oriented Bayesian networks. This training program is helpful for both beginners and intermediate professionals to enhance their skills in Bayesian networks.


Module 1: Parametric Bayesian Networks

  • Introduction
  • To understand the concept with an example - Discrete Random Variables
  • Conditional Independence
  • D-Separation in DAG
  • Concept of Markov Blanket
  • Parameters and Structural Learning
  • Probabilistic Logic Sampling Method
  • Computational Example using "bnlearn" package in R
  • Sampling From a Bayesian Network in R

Module 2: Estimate the Conditional Probability Distribution under known Network Structure

Module 3: Excellence in Bayesian Network Models

Module 4: Nonparametric Bayesian Network

  • To model the complex interactions in a multilevel structure
  • Bayesian Mixture Models
  • Optimal Junction Tree that represents a mixture
  • Applications of Dirichlet and Polya Tree Models in Bayesian Graphs
  • Applications in the different practical problems set up

Live Instructor-led & Interactive Online Sessions


Regular Course

Duration : 40 Hours


Capsule Course

Duration : 4-8 Hours

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Training Options

OPTION 1

Weekdays- Cloud Based Training

Mon - Fri 07:00 AM - 09:00 AM(Mon, Wed, Fri)

Weekdays Online Lab

Mon - Fri 07:00 AM - 09:00 AM(Tue, Thur)


OPTION 2

Weekend- Cloud Based Training

Sat-Sun 09:00 AM - 11:00 AM (IST)

Weekend Online Lab

Sat-Sun 11:00 AM - 01:00 PM


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