Databricks - Bayesian Inference with MCMC
5.0 /5- Offered byCoursera
Bayesian Inference with MCMC at Coursera Overview
Duration | 15 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Beginner |
Official Website | Explore Free Course  |
Credential | Certificate |
Bayesian Inference with MCMC at Coursera Highlights
- Shareable Certificate Earn a Certificate upon completion
- 100% online Start instantly and learn at your own schedule.
- Course 2 of 3 in the Introduction to Computational Statistics for Data Scientists Specialization
- Flexible deadlines Reset deadlines in accordance to your schedule.
- Beginner Level 1. Experience with Data Science using the PyData Stack of NumPy, SciPy, Pandas, Scikit-learn. 2. Course 1 in this Specialization.
- Approx. 15 hours to complete
- English Subtitles: English
Bayesian Inference with MCMC at Coursera Course details
- 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. The course website is located at https://sjster.github.io/introduction_to_computational_statistics/docs/index.html. The course notebooks can be downloaded from this website by following the instructions on page https://sjster.github.io/introduction_to_computational_statistics/docs/getting_started.html.
- The instructor for this course will be Dr. Srijith Rajamohan.
Bayesian Inference with MCMC at Coursera Curriculum
Topics in Model Performance
Welcome to Course 2!
Introduction
Underfitting and Overfitting
Explained Variance
Cross Validation
Information Criteria
Log-likelihood and Deviance
Posterior Predictive Distribution
AIC, BIC, DIC and WAIC
A qualitative discussion of the various metrics
Entropy
KL Divergence
Model Averaging
What can you expect from this course/specialization?
Likelihood and its use in Parameter Estimation and Model Comparison
Understanding predictive information criteria for Bayesian models
Information Theory and Statistics
Model Stacking
Topics in Model Performance
The Metropolis Algorithms for MCMC
Introduction
Markov Chains
Why does Markov Chain Monte Carlo work?
The Metropolis algorithm for sampling
The Metropolis algorithm in detail
Building the inferred distribution
Implementing the Metropolis algorithm in Python
The Metropolis-Hastings algorithm
Markov Chains
MCMC - I
Gibbs Sampling and Hamiltonian Monte Carlo Algorithms
Introduction to Gibbs sampling
Overview of the Gibbs Sampling algorithm
The Gibbs sampling algorithm in detail
Introduction to Hamiltonian Monte Carlo
The Hamiltonian Monte Carlo algorithm in detail
Properties of MCMC - I
Properties of MCMC - II
Hamiltonian Monte Carlo
HMC on Stan
MCMC - II
Bayesian Inference with MCMC at Coursera Admission Process
Important Dates
Other courses offered by Coursera
Bayesian Inference with MCMC at Coursera Students Ratings & Reviews
1 Rating- 4-51
Student Forum
Anything you would want to ask experts?