Bayesian Inference with MCMC

Go to Course: https://www.coursera.org/learn/mcmc

Introduction

**Course Review: Bayesian Inference with MCMC on Coursera** If you're diving into the world of Bayesian modeling and inference, the "Bayesian Inference with MCMC" course on Coursera is an essential stepping stone. This course serves as the second part of a three-course specialization that navigates the intricacies of Bayesian methods, focusing primarily on Markov Chain Monte Carlo (MCMC) techniques. ### Course Overview The primary objective of this course is to equip participants with a solid understanding of MCMC methods and their applications in Bayesian inference. Through an engaging blend of theory, hands-on examples, and programming exercises in Python using Jupyter notebooks, students will develop both conceptual and practical skills. From the outset, the course intends to lay the groundwork for Monte Carlo methods, gradually progressing to more advanced topics such as the Metropolis algorithms, Gibbs sampling, and Hamiltonian Monte Carlo. It deftly combines theoretical insights rooted in Information Theory with practical applications, ensuring that students are not only consumers of knowledge but also skilled practitioners. ### Curriculum Highlights 1. **Topics in Model Performance**: This module introduces various metrics for assessing model quality, seamlessly bridging concepts familiar to those with a machine learning background. It emphasizes the importance of understanding these metrics from an information-theoretic perspective, preparing participants for robust model evaluation. 2. **The Metropolis Algorithms for MCMC**: As the first concrete MCMC method discussed, this section offers a gentle introduction to Markov chains. It highlights the fundamentals of sampling from distributions, with a detailed exploration of the Metropolis and Metropolis-Hastings algorithms. With interactive Python implementations provided, students can observe firsthand how these methods operate. 3. **Gibbs Sampling and Hamiltonian Monte Carlo Algorithms**: The focus shifts to Gibbs sampling and the more complex Hamiltonian Monte Carlo in this module. While Gibbs sampling is explored in-depth, the course also introduces the foundational properties of MCMC, paving the way for more advanced applications in the third course. ### Practical Application Each module includes practical coding exercises that encourage learners to apply their newfound knowledge in real-world scenarios. The integration of Python and Jupyter notebooks throughout the course makes it easy to follow along and experiment with different modeling techniques. Specific details on downloading and running the notebooks are provided at the course's dedicated links, fostering an enriching learning experience. The structured guidance provided ensures that even those relatively new to programming can keep pace and engage effectively. ### Recommendations This course is highly recommended for: - **Data Scientists** who want to enhance their knowledge of Bayesian statistics and inferential methods. - **Statisticians** looking to incorporate MCMC techniques into their toolkit. - **Machine Learning practitioners** aspiring to deepen their understanding of Bayesian frameworks and model evaluation metrics. Having a prior understanding of basic statistical concepts and some proficiency in Python will significantly enhance the experience, but the course is designed to be approachable for motivated learners willing to engage with the material. ### Conclusion "Bayesian Inference with MCMC" on Coursera is a well-structured and informative course that dives deep into Bayesian modeling with MCMC methods. It successfully balances theoretical understanding with practical skills, ensuring that participants leave with valuable tools for their professional repertoire. Whether you're looking to boost your data analysis skills or broaden your statistical knowledge, this course is a compelling choice that is sure to deliver. For more details and to enroll, visit the [course website](https://sjster.github.io/introduction_to_computational_statistics/docs/Production/BayesianInference.html). The hands-on experience, coupled with the comprehensive syllabus, makes this course a worthwhile investment in your educational journey.

Syllabus

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. 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

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

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