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We reviewed 3 live results for bayesian kinetics and narrowed them down to the 3 options that look most worth comparing first.
The options are Bayesian Statistics, Bayes Rules!: An Introduction to Applied Bayesian Modeling and Bayesian Inference Fundamentals.
Source: Duke University
Description
Bayesian Statistics is an online, self-paced course offered by Duke University's Department of Statistical Science as part of the Statistics with R Coursera Specialization. The course covers Bayesian inference, transforming prior probabilities into posterior probabilities with Bayes' rule, and practical modeling. Learners explore end-to-end analyses in R, credible regions, comparisons of means and proportions, Bayesian regression, multi-model inference, and prediction under the instruction of Duke faculty including Mine Çetinkaya-Rundel, David Banks, Colin Rundel, and Merlise Clyde.
Best for
data analysts, statisticians, learning Bayesian statistical modeling and implementing Bayesian inference in R
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Source: Routledge
Description
Bayes Rules!: An Introduction to Applied Bayesian Modeling by Alicia A. Johnson, Miles Q. Ott, and Mine Dogucu is a textbook published by Chapman & Hall. Designed for advanced undergraduate statistics students and practitioners, this 544-page introductory guide covers Bayesian thinking, conjugate families, Markov chain Monte Carlo simulation, regression, and hierarchical modeling with integrated R code, RStan tools, and the bayesrules package.
Best for
Advanced undergraduate statistics students, Data science practitioners, Introductory undergraduate Bayesian statistics courses and Self-study in applied Bayesian modeling and computation
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Source: Coursera
Description
Bayesian Inference Fundamentals is an intermediate online course offered by the University of Pittsburgh on Coursera and taught by Konstantinos Pelechrinis. Part of the Applied Bayesian Data Analysis Specialization, the program covers Bayes' theorem, prior and posterior distributions, conjugate priors, credible intervals, and rejection sampling. Learners also study Markov Chain Monte Carlo computational methods, including Gibbs sampling and Metropolis-Hastings algorithms, completing 4 modules with 23 assignments, readings, and guided Python labs.
Best for
intermediate data science learners, statisticians, learning Bayesian statistical reasoning and implementing MCMC algorithms in Python
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| Compare | #1Bayesian Statistics | #2Bayes Rules!: An Introduction to Applied Bayesian Modeling | #3Bayesian Inference Fundamentals |
|---|---|---|---|
| Source | Duke University | Routledge | Coursera |
| Description | Bayesian Statistics is an online, self-paced course offered by Duke University's Department of Statistical Science as part of the Statistics with R Coursera Specialization. The course covers Bayesian inference, transforming prior probabilities into posterior probabilities with Bayes' rule, and practical modeling. Learners explore end-to-end analyses in R, credible regions, comparisons of means and proportions, Bayesian regression, multi-model inference, and prediction under the instruction of Duke faculty including Mine Çetinkaya-Rundel, David Banks, Colin Rundel, and Merlise Clyde. | Bayes Rules!: An Introduction to Applied Bayesian Modeling by Alicia A. Johnson, Miles Q. Ott, and Mine Dogucu is a textbook published by Chapman & Hall. Designed for advanced undergraduate statistics students and practitioners, this 544-page introductory guide covers Bayesian thinking, conjugate families, Markov chain Monte Carlo simulation, regression, and hierarchical modeling with integrated R code, RStan tools, and the bayesrules package. | Bayesian Inference Fundamentals is an intermediate online course offered by the University of Pittsburgh on Coursera and taught by Konstantinos Pelechrinis. Part of the Applied Bayesian Data Analysis Specialization, the program covers Bayes' theorem, prior and posterior distributions, conjugate priors, credible intervals, and rejection sampling. Learners also study Markov Chain Monte Carlo computational methods, including Gibbs sampling and Metropolis-Hastings algorithms, completing 4 modules with 23 assignments, readings, and guided Python labs. |
| Best for | data analysts, statisticians, learning Bayesian statistical modeling and implementing Bayesian inference in R | Advanced undergraduate statistics students, Data science practitioners, Introductory undergraduate Bayesian statistics courses and Self-study in applied Bayesian modeling and computation | intermediate data science learners, statisticians, learning Bayesian statistical reasoning and implementing MCMC algorithms in Python |
| Action | Duke University | Routledge | Coursera |
| Rate this product |
"Bayesian Statistics from Duke University."
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