Learn about prior, likelihood, posterior; Bayes' theorem; simple inference with PyMC3; and Bayesian model fitting (regression) with PyMC3.
- [Instructor] The last topic in this course…is Bayesian inference,…a type of statistical inference…that has been gaining more and more interest in adoption…over the last few decades.…Of course I won't be able to do it justice in a few minutes,…but I wanted to at least introduce it…because it's the kind of statistics…that I do every day in my job.…I hope I can at least make you curious…to learn about it elsewhere.…Bayesian inference takes a very different viewpoint…from anything else we've seen so far.…Instead of estimating from the data a single value…for the population parameters,…we characterize them with entire probability distributions…which represent our knowledge…and our uncertainty about them.…
So we start with the prior probability…which represents what we already know about the parameters,…if anything.…We make observations, and we use the observations…to update the prior into posterior probability.…Here's a silly example.…Suppose my cat hid behind one of two doors,…I don't know which,…so my priors were probability of 50% for each door.…
- Installing and setting up Python
- Importing and cleaning data
- Visualizing data
- Describing distributions and categorical variables
- Using basic statistical inference and modeling techniques
- Bayesian inference
Skill Level Intermediate
SPSS Statistics Essential Trainingwith Barton Poulson4h 57m Beginner
R Statistics Essential Trainingwith Barton Poulson5h 59m Intermediate
1. Installation and Setup
2. Importing and Cleaning Data
3. Visualizing and Describing Data
4. Introduction to Statistical Inference
5. Introduction to Statistical Modeling
Next steps1m 55s
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