Bayesian Statistics | Full University Course
About this Course
This Course is intended for all learners seeking to develop proficiency in statistics, Bayesian statistics, Bayesian inference, R programming, and much more. Through four complete courses (From Concept to Data Analysis; Techniques and Models; Mixture Models; Time Series Analysis) and a culminating project, you will cover Bayesian methods — such as conjugate models, MCMC, mixture models, and dynamic linear modeling — which will provide you with the skills necessary to perform analysis, engage in forecasting, and create statistical models using real-world data.
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Bayesian Statistics: From Concept to Data Analysis
0:00:00 Module overview
0:04:15 Probability
0:14:09 Bayes theorem
0:24:55 Review of distributions
0:38:20 Frequentist inference
1:14:14 Bayesian inference
1:37:25 Priors
1:50:33 Bernoulli binomial data
2:35:13 Poisson data
2:43:40 Exponential data
2:47:43 Normal data
2:54:42 Alternative priors
3:06:16 Linear regression
3:51:15 Course conclusion
Bayesian Statistics: Techniques and Models
3:52:16 Module overview
3:58:02 Statistical modeling
4:14:28 Bayesian modeling
4:46:50 Monte carlo estimation
5:31:15 Metropolis hastings
6:21:52 Jags
6:38:05 Gibbs sampling
7:14:06 Assessing convergence
7:40:06 Linear regression
8:41:43 Anova
9:11:16 Logistic regression
9:51:01 Poisson regression
10:23:29 Hierarchical modeling
11:23:21 Mixture models
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