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Course10610484461 lectures

Bayesian Data Analysis for the Behavioral Sciences

Prof. Himanshu Yadav · IIT Kanpur

Start with lecture 1 At NPTEL

Lectures

Week 1

  1. Lecture 1: Sets Video
  2. Lecture 2: Probability Axioms Video
  3. Lecture 3:Probability Assignment Video
  4. Lecture 4:Probability Assigner Functions Video
  5. Lecture 5: Random Variables Video

Week 2

  1. Lecture 6: Probability Distributions and Statistical Inference Video
  2. Lecture 7: Discrete Random variables: Binomial and Poisson Distributions Video
  3. Lecture 8: Continuous Random Variables Video
  4. Lecture 9: Normal Distribution Video
  5. Lecture 10: Lognormal Distribution and Beta Distribution Video

Week 3

  1. Lecture 11: Probability Distributions in R Video
  2. Lecture 12: Likelihood Function Video
  3. Lecture 13: Graph of a Likelihood Function Video
  4. Lecture 14: Conditional Probability Video
  5. Lecture 15: Bayes' Theorem Video

Week 4

  1. Lecture 16: Bayesian Inference Video
  2. Lecture 17: Likelihood, Prior and Posterior in R Video
  3. Lecture 18: How the data, prior affects the posterior Video
  4. Lecture 19: Prior & Posterior Predictions Video
  5. Lecture 20: Generating Prior and Posterior Prediction in R Video

Week 5

  1. Lecture 21: Posterior Predictive Checks Video
  2. Lecture 22: Model Building in the Bayesian Framework Video
  3. Lecture 23: Bayesian Modeling Workflow in R Video
  4. Lecture 24: Bayesian Learning Video
  5. Lecture 25: Specifying Priors Video

Week 6

  1. Lecture 26: Parameter Estimation Method Video
  2. Lecture 27: Analytical Methods of Posterior Estimation Video
  3. Lecture 28: Grid Approximation Video
  4. Lecture 29: Importance Sampling Video
  5. Lecture 30: Introduction to Markov Chain Monte Carlo Video

Week 7

  1. Lecture 31: MCMC Sampler Video
  2. Lecture 32: MCMC Implementation Video
  3. Lecture 33: MCMC Diagnostics Video
  4. Lecture 34: MCMC Examples Video
  5. Lecture 35: MCMC Sampling for Data with Multiple Observations Video

Week 8

  1. Lecture 36: Hamiltonian Monte Carlo Video
  2. Lecture 37: HMC Implimentation Video
  3. Lecture 38: HMC Example Video
  4. Lecture 39: Introduction to brms Video
  5. Lecture 40: Parameter Estimation using brms Video

Week 9

  1. Lecture 41: Introduction to Regression Models Video
  2. Lecture 42: Regression Modeling using brms Video
  3. Lecture 43: Logistic & Poisson Regression Video
  4. Lecture 44: Regression Modelling Examples Video
  5. Lecture 45: Interaction Effect & Nested Contrast Video

Week 10

  1. Lecture 46: Posterior Predictive Checks Video
  2. Lecture 47: Information- Theoretic Measures of Predictive Accuracy Video
  3. Lecture 48: Expected Log Predictive Density (ELPD) Video
  4. Lecture 49: Cross Validation Video
  5. Lecture 50: Cross - Validation Implementation Video

Week 11

  1. Lecture 51: Validations with Brms Video
  2. Lecture 52: Bayes Factor Video
  3. Lecture 53: Bayes factor Implementation Video
  4. Lecture 54: Hypothesis Testing using Bayes Factors Video
  5. Lecture 55: Computing Bayes Factor using Bridge Sampling Video
  6. Feedback for Week 11 Video

Week 12

  1. Lecture 56: Bayesian Hierarchical Models Video
  2. Lecture 57: Hierarchical Regression Models Video
  3. Lecture 58:Individual Difference Models Video
  4. Lecture 59: Hierarchical Modeling with brms Video
  5. Lecture 60: Theory Testing & Computational Modeling Video

Syllabus and materials

At NPTEL, as the course publishes them.

Credits and licence

Institution
IIT Kanpur
Department
Computer Science and Engineering
Teacher
Prof. Himanshu Yadav
Level
Undergraduate
Language
English
Subjects
Computer science, Engineering
Source
NPTEL
Licence
CC BY-NC-SA, as NPTEL states it. Non-commercial use only. NPTEL's terms
Attribution
NPTEL states: "Distributed under Creative Commons Attribution-ShareAlike / CC BY - NC - SA", beside a BY-NC-SA badge.
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