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  2. MCMC, easy as 123: Getting ...

MCMC, easy as 123: Getting started with Bayesian Analysis - 2021 AM Microlearning Series

No article about the explosion of Bayesian analysis is complete without mentioning that this boom is the result of increased computing power. But what is all that extra computation actually doing? This session will clarify what’s happening within a Markov chain Monte Carlo (MCMC) algorithm. Along the way, they’ll learn to understand the difference between “pen and paper” and “keyboard and click” algorithms, and to open themselves to the latter.

CAS provides closed captioning generated by software applications for the convenience of viewers. CAS does not review for accuracy of the information that appears in a closed caption, and such information (including any foreign language translation and transcription displayed in a closed caption) may not be relied upon as an accurate or complete record of CAS programming. CAS makes no representations or warranties, and expressly disclaim any responsibility or liability with respect to, any errors or omissions in, or the accuracy, reliability, timeliness or completeness of, any information that appears in a closed caption

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Introduction

Presentation: Brian Fannin, 8 min 4 sec

Histograms and Probability Functions

Presentation: Brian Fannin, 5 min 54 sec

The Kernel

Presentation: Brian Fannin, 10 min 10 sec

Exploring the Kernel

Presentation: Brian Fannin, 13 min 25 sec

Parameter Estimation

Presentation: Brian Fannin, 7 min 16 sec

Bayesian Parameter Estimation

Presentation: Brian Fannin, 13 min 25 sec

Bayes and Metropolis

Presentation: Brian Fannin, 5 min 39 sec

Markov Chains and GLMs

Presentation: Brian Fannin, 6 min 19 sec

Say hello to my little friend... Stan

Presentation: Brian Fannin, 9 min 34 sec

Conclusion

Presentation: Brian Fannin, 2 min 21 sec

MCMC, easy as 123 Series Feedback

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