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This was really insightful. Thank you.

Thank you for this lecture! I find most videos on Bayesian stats are either too vague or too technical, this hits the sweet spot for me. I love how you break down the prior, prior probability distribution, and marginal probability before going into Bayes theorem. Toward the end, you talked about how Bayesian statistics can provide evidence for the null, in the form of the Bayes Factor. I was wondering: if you get a credible interval that includes zero for a parameter estimate, does this also provide evidence for the null (that this parameter is not significantly different from zero), or would you still have to calculate the Bayes Factor?

This was great 👍 one of the best explanations

Amaizing lecture, thank you Angelika

This video ROCKS ! Thank you very much, I wish you all the best : -)

This is awesome! I love this channel! An absolute gem!

Nice review of basic Bayes. I would have like to see more on R roles in working with Bayes - for instance rstan. Recently a new package called bayesrules came out which looks user friendly. Thanks

Thanks for this highly helpful introductory workshop on Bayesian Statistics. 0:00 Introduction of the speaker 4:15 Outline Workshop 6:49 History of - and Philosophy behind Bayesian Statistics Possibility to differentiate between Parameters Estimation and Hypothesis Testing [PARAMETER ESTIMATION] 12:25 Parameter Estimation 15:09 Example Datta (Dog vs. Cat Person) 17:05 Prior Distribution: Definition and Examples 22:16 R Hands-On for a Beta Prior Distribution 23:49 Audience-Question: Regarding Beta Distribution 31:12 Likelihood Explained: examples in binominal distribution; What does Theta mean? 38:40 Prior Predictive Distribution 47:00 Two Exemplary Prior Predictive Distributions (in Dog vs. Cat Person) 48:49 Introducing Data to the Model: Create Example Data 50:07 Marginal Likeliehood 52:13 Posterior Distribution 1:02:58 Credible Interval [HYPOTHESIS TESTING] or MODEL COMPARISON 1:08:54 Hypothesis Testing / Model Comparison 1:11:35 Prior Model Odds (Before Data Collection) 1:15:19 Example Data (Dog vs. Cat Person) / Bayes Factor 1:20:15 Interpretation of Bayes Factor & Taxonomy 1:24:43 Posterior Model Odds 1:32:25 Outlook / More Complex Cases The direct link to the slides (in the video desciption) appears to be broken for me. However I found them on github.com/rladiesamsterdam/2021_Sept_Bayesian_statistics/blob/main/2021-09-28_Bayes_R-Ladies.pdf

great!

Thanks ! Very nice presentation

Once again, many thanks it was a great session.