Course details

Bayesian Methods: Implementing Bayesian Model and Computation with PyMC

Bayesian Methods: Implementing Bayesian Model and Computation with PyMC


Overview/Description
Expected Duration
Lesson Objectives
Course Number
Expertise Level



Overview/Description

Learners can examine the concept of Bayesian learning and the different types of Bayesian models in this 12-video course. Discover how to implement Bayesian models and computations by using different approaches and PyMC for your machine learning solutions. Learners start by exploring critical features of and difficulties associated with Bayesian learning methods, and then take a look at defining the Bayesian model and classifying single-parameter, multiparameter, and hierarchical Bayesian models. Examine the features of probabilistic programming and learn to list the popular probabilistic programming languages. You will look at defining Bayesian models with PyMC and arbitrary deterministic function and generating posterior samples with PyMC models. Next, learners recall the fundamental activities involved in the PyMC Bayesian data analysis process, including model checking, evaluation, comparison, and model expansion. Delve into the computation methods of Bayesian, including numerical integration, distributional approximation, and direct simulation. Also, look at computing with Markov chain simulation, and the prominent algorithms that can be used to find posterior modes based on the distribution approximation. The concluding exercise focuses on Bayesian modeling with PyMC.



Expected Duration (hours)
0.8

Lesson Objectives

Bayesian Methods: Implementing Bayesian Model and Computation with PyMC

  • discover the key concepts covered in this course
  • identify critical features of and the difficulties associated with Bayesian learning methods
  • define the Bayesian model and classify single-parameter, multi-parameter, and hierarchical Bayesian models
  • describe the features of probabilistic programming and list the popular probabilistic programming languages
  • use PyMC to define a model and arbitrary deterministic function and use the model to generate posterior samples
  • recall the fundamental activities involved in Bayesian data analysis process, including model checking, evaluation, comparison, and model expansion
  • implement Bayesian data analysis with PyMC using the rejection sampling approach
  • recognize the essential approaches that can be used to implement Bayesian computation, including numerical integration, distributional approximation, and direct simulation
  • describe Markov chain simulation and how it is used for computations
  • implement Markov chain simulation using Python
  • list the prominent algorithms that can be used to find posterior modes based on the distribution approximation
  • specify the essential features of probabilistic programming, recall the approaches that can be used to implement Bayesian computation, and implement Bayesian data analysis using PyMC
  • Course Number:
    it_mlbmmldj_02_enus

    Expertise Level
    Intermediate