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.
This course is part of the Introduction to Computational Statistics for Data Scientists Specialization
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About this Course
1. Experience with Data Science using the PyData Stack of NumPy, SciPy, Pandas, Scikit-learn.
2. Course 1 in this Specialization.
Could your company benefit from training employees on in-demand skills?
Try Coursera for BusinessWhat you will learn
1. Markov Chain Monte Carlo algorithms
2. Implementing the above in Python
3. Assess the performance of Bayesian models
Skills you will gain
- Bayesian
- Scipy
- Scikit-Learn
- MCMC
1. Experience with Data Science using the PyData Stack of NumPy, SciPy, Pandas, Scikit-learn.
2. Course 1 in this Specialization.
Could your company benefit from training employees on in-demand skills?
Try Coursera for BusinessOffered by
Syllabus - What you will learn from this course
Topics in Model Performance
The Metropolis Algorithms for MCMC
Gibbs Sampling and Hamiltonian Monte Carlo Algorithms
About the Introduction to Computational Statistics for Data Scientists Specialization

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