Learn · Bayesian Data Analysis for the Behavioral Sciences

Lecture 43 of 61Week 9

Lecture 43: Logistic & Poisson Regression

Prof. Himanshu Yadav · IIT Kanpur

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Bayesian Data Analysis for the Behavioral Sciences, IIT Kanpur (at NPTEL)
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All the lectures

  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
  6. Lecture 6: Probability Distributions and Statistical Inference Video
  7. Lecture 7: Discrete Random variables: Binomial and Poisson Distributions Video
  8. Lecture 8: Continuous Random Variables Video
  9. Lecture 9: Normal Distribution Video
  10. Lecture 10: Lognormal Distribution and Beta Distribution Video
  11. Lecture 11: Probability Distributions in R Video
  12. Lecture 12: Likelihood Function Video
  13. Lecture 13: Graph of a Likelihood Function Video
  14. Lecture 14: Conditional Probability Video
  15. Lecture 15: Bayes' Theorem Video
  16. Lecture 16: Bayesian Inference Video
  17. Lecture 17: Likelihood, Prior and Posterior in R Video
  18. Lecture 18: How the data, prior affects the posterior Video
  19. Lecture 19: Prior & Posterior Predictions Video
  20. Lecture 20: Generating Prior and Posterior Prediction in R Video
  21. Lecture 21: Posterior Predictive Checks Video
  22. Lecture 22: Model Building in the Bayesian Framework Video
  23. Lecture 23: Bayesian Modeling Workflow in R Video
  24. Lecture 24: Bayesian Learning Video
  25. Lecture 25: Specifying Priors Video
  26. Lecture 26: Parameter Estimation Method Video
  27. Lecture 27: Analytical Methods of Posterior Estimation Video
  28. Lecture 28: Grid Approximation Video
  29. Lecture 29: Importance Sampling Video
  30. Lecture 30: Introduction to Markov Chain Monte Carlo Video
  31. Lecture 31: MCMC Sampler Video
  32. Lecture 32: MCMC Implementation Video
  33. Lecture 33: MCMC Diagnostics Video
  34. Lecture 34: MCMC Examples Video
  35. Lecture 35: MCMC Sampling for Data with Multiple Observations Video
  36. Lecture 36: Hamiltonian Monte Carlo Video
  37. Lecture 37: HMC Implimentation Video
  38. Lecture 38: HMC Example Video
  39. Lecture 39: Introduction to brms Video
  40. Lecture 40: Parameter Estimation using brms Video
  41. Lecture 41: Introduction to Regression Models Video
  42. Lecture 42: Regression Modeling using brms Video
  43. Lecture 43: Logistic & Poisson Regression Video
  44. Lecture 44: Regression Modelling Examples Video
  45. Lecture 45: Interaction Effect & Nested Contrast Video
  46. Lecture 46: Posterior Predictive Checks Video
  47. Lecture 47: Information- Theoretic Measures of Predictive Accuracy Video
  48. Lecture 48: Expected Log Predictive Density (ELPD) Video
  49. Lecture 49: Cross Validation Video
  50. Lecture 50: Cross - Validation Implementation Video
  51. Lecture 51: Validations with Brms Video
  52. Lecture 52: Bayes Factor Video
  53. Lecture 53: Bayes factor Implementation Video
  54. Lecture 54: Hypothesis Testing using Bayes Factors Video
  55. Lecture 55: Computing Bayes Factor using Bridge Sampling Video
  56. Feedback for Week 11 Video
  57. Lecture 56: Bayesian Hierarchical Models Video
  58. Lecture 57: Hierarchical Regression Models Video
  59. Lecture 58:Individual Difference Models Video
  60. Lecture 59: Hierarchical Modeling with brms Video
  61. Lecture 60: Theory Testing & Computational Modeling Video