# Implementing Bayesian Model and Computation with PyMC

Bayesian statistics    |    Intermediate
• 12 videos | 47m 29s
• Includes Assessment
Rating 4.8 of 5 users (5)
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

• In this video, find out how to identify critical features of Bayesian learning methods and the difficulties associated with them.
• 3.  Bayesian Model Types
In this video, you will define the Bayesian model and classify single-parameter, multi-parameter, and hierarchical Bayesian models.
• 4.  Probabilistic Programming
After completing this video, you will be able to describe the features of probabilistic programming and list popular probabilistic programming languages.
• 5.  Modeling with PyMC
In this video, find out how to use PyMC to define a model and an arbitrary deterministic function, and use the model to generate posterior samples.
• 6.  Bayesian Data Analysis Process
Upon completion of this video, you will be able to recall the fundamental activities involved in the Bayesian data analysis process, including model checking, evaluation, comparison, and model expansion.
• 7.  Bayesian Data Analysis with PyMC
During this video, you will learn how to implement Bayesian data analysis with PyMC using the rejection sampling approach.
• 8.  Bayesian Computation Methods
After completing this video, you will be able to recognize the essential approaches that can be used to implement Bayesian computation, including numerical integration, distributional approximation, and direct simulation.
• 9.  Markov Chain Simulation
After completing this video, you will be able to describe Markov chain simulations and how they are used for computations.
• 10.  Implementing Markov Chain Simulation
In this video, you will learn how to implement Markov chain simulation using Python.
• 11.  Finding Posterior Modes
After completing this video, you will be able to list the prominent algorithms that can be used to find posterior modes based on the distribution approximation.
• 12.  Exercise: Bayesian Modeling with PyMC
After completing this video, you will be able to 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.

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