Bayesian Inference with MCMC (Coursera)

Bayesian Inference with MCMC (Coursera)
Course Auditing
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Effort
Certification
Languages
1. Experience with Data Science using the PyData Stack of NumPy, SciPy, Pandas, Scikit-learn. 2. Course 1 in this Specialization.
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Bayesian Inference with MCMC (Coursera)
The objective of this course is to introduce Markov Chain Monte Carlo Methods for Bayesian modeling and inference, The attendees will start off by learning the the basics of Monte Carlo methods. This will be augmented by hands-on examples in Python that will be used to illustrate how these algorithms work. This will be the second course in a specialization of three courses .Python and Jupyter notebooks will be used throughout this course to illustrate and perform Bayesian modeling with PyMC3.

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What You Will Learn

1. Markov Chain Monte Carlo algorithms

2. Implementing the above in Python

3. Assess the performance of Bayesian models


Course 2 of 3 in the Introduction to Computational Statistics for Data Scientists Specialization


Syllabus


WEEK 1

Topics in Model Performance

This module gives an overview of topics related to assessing the quality of models. While some of these metrics may be familiar to those with a Machine Learning background, the goal is to bring awareness to the concepts rooted in Information Theory.


WEEK 2

The Metropolis Algorithms for MCMC

This module serves as a gentle introduction to Markov-Chain Monte Carlo methods. The general idea behind Markov chains are presented along with their role in sampling from distributions. The Metropolis and Metropolis-Hastings algorithms are introduced and implemented in Python to help illustrate their details.


WEEK 3

Gibbs Sampling and Hamiltonian Monte Carlo Algorithms

This module is a continuation of module 2 and introduces Gibbs sampling and the Hamiltonian Monte Carlo (HMC) algorithms for inferring distributions. The Gibbs sampler algorithm is illustrated in detail, while the HMC receives a more high-level treatment due to the complexity of the algorithm. Finally, some of the properties of MCMC algorithms are presented to set the stage for Course 3 which uses the popular probabilistic framework PyMC3.



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Course Auditing
41.00 EUR/month
1. Experience with Data Science using the PyData Stack of NumPy, SciPy, Pandas, Scikit-learn. 2. Course 1 in this Specialization.

MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.