Implementing Bayesian Model and Computation with PyMC

Bayesian statistics
  • 12 Videos | 52m 29s
  • Includes Assessment
  • Earns a Badge
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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.

WHAT YOU WILL LEARN

  • 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

IN THIS COURSE

  • Playable
    1. 
    Course Overview
    1m 59s
    UP NEXT
  • Playable
    2. 
    Bayesian Learning
    6m 2s
  • Locked
    3. 
    Bayesian Model Types
    3m 50s
  • Locked
    4. 
    Probabilistic Programming
    7m 5s
  • Locked
    5. 
    Modeling with PyMC
    5m 53s
  • Locked
    6. 
    Bayesian Data Analysis Process
    5m 30s
  • Locked
    7. 
    Bayesian Data Analysis with PyMC
    4m 8s
  • Locked
    8. 
    Bayesian Computation Methods
    2m 55s
  • Locked
    9. 
    Markov Chain Simulation
    2m 12s
  • Locked
    10. 
    Implementing Markov Chain Simulation
    2m 29s
  • Locked
    11. 
    Finding Posterior Modes
    2m 21s
  • Locked
    12. 
    Exercise: Bayesian Modeling with PyMC
    3m 5s

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