5. Bayesian modelling

5.1 Bayes theorem

1. Intuition. Updating knowledge with new information Video   ES      11 mins

2. Intepretating the Bayes theorem Video   ES      7 mins  

3. Example case. A medical test under Bayes theorem Video   ES      8 mins  

4. Example case. Visual interpretation Video   ES      9 mins  

5. Example case. Impact of changing initial conditions Video   ES      6 mins  

6. Bayes theorem with discrete distributionsVideo   PENDING Notebook

7. Terminology on Bayes theorem Video   ES      8 mins  

8. Priors and posteriors on consecutive observations Video   ES      11 mins

9. Bayes theorem on continuous distributionsVideo   PENDING

10. Updating knowledge on continuous distributionsVideo   PENDING

11. Bayes theorem with continuous distributionsVideo   PENDING Notebook

LAB 01 Bayes posterior and classifiersVideo   PENDING Notebook

5.2 Bayesian inference

1. Introduction to Bayesian inference and modellingVideo   PENDING

2. Modelling a coin flip with a discrete priorVideo   PENDING Notebook

3- Conjugate distributionsVideo   PENDING

4. Conjugate distributions for noisy measurementsVideo   PENDING Notebook

5. Markov chain Monte CarloVideo   PENDING

6. MCMC in TF Probability (not needed?)Video   PENDING Notebook

7. Modelling a coin flip with TF ProbabilityVideo   PENDING Notebook

LAB 02 Bayesian modelling of a DiceVideo   PENDING Notebook

LAB 03 Truncated regressionVideo   PENDING Notebook

1. Intuition. Updating knowledge with new information

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2. Intepretating the Bayes theorem

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3. Example case. A medical test under Bayes theorem

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4. Example case. Visual interpretation

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5. Example case. Impact of changing initial conditions

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7. Terminology on Bayes theorem

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8. Priors and posteriors on consecutive observations

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