Databricks

Bayesian Inference with MCMC

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Gain insight into a topic and learn the fundamentals.
3.3

(20 reviews)

Beginner level

Recommended experience

14 hours to complete
3 weeks at 4 hours a week
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
3.3

(20 reviews)

Beginner level

Recommended experience

14 hours to complete
3 weeks at 4 hours a week
Flexible schedule
Learn at your own pace

What you'll learn

  • 1. Markov Chain Monte Carlo algorithms

    2. Implementing the above in Python

    3. Assess the performance of Bayesian models

Details to know

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Assessments

3 assignments

Taught in English

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This course is part of the Introduction to Computational Statistics for Data Scientists Specialization
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There are 3 modules in this course

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. The course website is https://sjster.github.io/introduction_to_computational_statistics/docs/Production/BayesianInference.html. Instructions to download and run the notebooks are at https://sjster.github.io/introduction_to_computational_statistics/docs/Production/getting_started.html

What's included

13 videos5 readings1 assignment

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. The course website is https://sjster.github.io/introduction_to_computational_statistics/docs/Production/MonteCarlo.html. Instructions to download and run the notebooks are at https://sjster.github.io/introduction_to_computational_statistics/docs/Production/getting_started.html

What's included

8 videos1 reading1 assignment2 plugins

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. The course website is https://sjster.github.io/introduction_to_computational_statistics/docs/Production/MonteCarlo.html#gibbs-sampling. Instructions to download and run the notebooks are at https://sjster.github.io/introduction_to_computational_statistics/docs/Production/getting_started.html

What's included

7 videos2 readings1 assignment1 plugin

Instructor

Instructor ratings
1.7 (7 ratings)
Dr. Srijith Rajamohan
Databricks
3 Courses6,932 learners

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