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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[shafqat]

Thank you so much for this! I'm a junior Data Scientist looking to develop my prowess in Applied Bayesian Statistics. This is a lifesaver!

[deborahfranza]

The fact that you described what the squiggly line was with the dot above and below @39:50 easily puts you ahead of th emajority of professors in my university. Most wouldn't even pause to clarify that.

[hari]

Sincerely add the mixture model and hierarchical modeling part thank you

[antonmas]

where can i get supplementary material?

[OriginalJoseyWales]

Will this course help me go do Bayesian analysis using R?

[chupachups]

Thank you for believing in free knowledge

[inquinimento]

I got confused pretty much from the beginning about willingness to take a bet where I gain $4if it rains and loose $1 if it doesn’t. I don’t see how my willingness to take that bet at those amounts translates into by subjective belief about the probability of the event itself (rain). The shift from size of bet to probably that the event leading to a payoff will occur seems confusing to me.