# Bayesian Data Analysis for the Behavioral Sciences

- Institution: IIT Kanpur
- Department: Computer Science and Engineering
- Teacher: Prof. Himanshu Yadav
- Level: Undergraduate
- Language: English
- Subjects: Computer science, Engineering
- Source: [NPTEL](https://nptel.ac.in/courses/106104844)
- Licence: [CC BY-NC-SA](https://nptel.ac.in/faq) (Non-commercial use only.)
- Syllabus: [at the source](https://nptel.ac.in/courses/106104844)
- Who can watch: Tweens profiles and older (sources give no ratings; placed by Invisible Network from the source's own words)

## Lectures

1. [Lecture 1: Sets](https://invisiblenetwork.ai/learn/nptel/106104844/0b70dc14a397) (Video)
2. [Lecture 2: Probability Axioms](https://invisiblenetwork.ai/learn/nptel/106104844/ebbe3862b9e0) (Video)
3. [Lecture 3:Probability Assignment](https://invisiblenetwork.ai/learn/nptel/106104844/5b57dee115cb) (Video)
4. [Lecture 4:Probability Assigner Functions](https://invisiblenetwork.ai/learn/nptel/106104844/5caac4096270) (Video)
5. [Lecture 5: Random Variables](https://invisiblenetwork.ai/learn/nptel/106104844/db01ce0a2d20) (Video)
6. [Lecture 6: Probability Distributions and Statistical Inference](https://invisiblenetwork.ai/learn/nptel/106104844/4154574310f8) (Video)
7. [Lecture 7: Discrete Random variables: Binomial and Poisson Distributions](https://invisiblenetwork.ai/learn/nptel/106104844/c43aecfa606f) (Video)
8. [Lecture 8: Continuous Random Variables](https://invisiblenetwork.ai/learn/nptel/106104844/3933a1a76abc) (Video)
9. [Lecture 9: Normal Distribution](https://invisiblenetwork.ai/learn/nptel/106104844/fee8e798535d) (Video)
10. [Lecture 10: Lognormal Distribution and Beta Distribution](https://invisiblenetwork.ai/learn/nptel/106104844/f743c74ead47) (Video)
11. [Lecture 11: Probability Distributions in R](https://invisiblenetwork.ai/learn/nptel/106104844/74f74d6a623b) (Video)
12. [Lecture 12: Likelihood Function](https://invisiblenetwork.ai/learn/nptel/106104844/294e93dbb9cc) (Video)
13. [Lecture 13: Graph of a Likelihood Function](https://invisiblenetwork.ai/learn/nptel/106104844/3b36e2cdabaa) (Video)
14. [Lecture 14: Conditional Probability](https://invisiblenetwork.ai/learn/nptel/106104844/d9e0a88c352d) (Video)
15. [Lecture 15: Bayes' Theorem](https://invisiblenetwork.ai/learn/nptel/106104844/1d1969c83657) (Video)
16. [Lecture 16: Bayesian Inference](https://invisiblenetwork.ai/learn/nptel/106104844/d322054fec85) (Video)
17. [Lecture 17: Likelihood, Prior and Posterior in R](https://invisiblenetwork.ai/learn/nptel/106104844/f07ea8aee7ef) (Video)
18. [Lecture 18: How the data, prior affects the posterior](https://invisiblenetwork.ai/learn/nptel/106104844/70ad9eba565d) (Video)
19. [Lecture 19: Prior & Posterior Predictions](https://invisiblenetwork.ai/learn/nptel/106104844/de444b2791e7) (Video)
20. [Lecture 20: Generating Prior and Posterior Prediction in R](https://invisiblenetwork.ai/learn/nptel/106104844/cb04620601b1) (Video)
21. [Lecture 21: Posterior Predictive Checks](https://invisiblenetwork.ai/learn/nptel/106104844/86c761b0a19b) (Video)
22. [Lecture 22: Model Building in the Bayesian Framework](https://invisiblenetwork.ai/learn/nptel/106104844/a4abde9cf104) (Video)
23. [Lecture 23: Bayesian Modeling Workflow in R](https://invisiblenetwork.ai/learn/nptel/106104844/52046eee72e9) (Video)
24. [Lecture 24: Bayesian Learning](https://invisiblenetwork.ai/learn/nptel/106104844/f7e670aac059) (Video)
25. [Lecture 25: Specifying Priors](https://invisiblenetwork.ai/learn/nptel/106104844/caa22d9d6150) (Video)
26. [Lecture 26: Parameter Estimation Method](https://invisiblenetwork.ai/learn/nptel/106104844/3e64fa0890d8) (Video)
27. [Lecture 27: Analytical Methods of Posterior Estimation](https://invisiblenetwork.ai/learn/nptel/106104844/f94089863f7d) (Video)
28. [Lecture 28: Grid Approximation](https://invisiblenetwork.ai/learn/nptel/106104844/59202f74bd64) (Video)
29. [Lecture 29: Importance Sampling](https://invisiblenetwork.ai/learn/nptel/106104844/03ff24e12f61) (Video)
30. [Lecture 30: Introduction to Markov Chain Monte Carlo](https://invisiblenetwork.ai/learn/nptel/106104844/3a3778b30594) (Video)
31. [Lecture 31: MCMC Sampler](https://invisiblenetwork.ai/learn/nptel/106104844/388bc6446157) (Video)
32. [Lecture 32: MCMC Implementation](https://invisiblenetwork.ai/learn/nptel/106104844/128f739d5d14) (Video)
33. [Lecture 33: MCMC Diagnostics](https://invisiblenetwork.ai/learn/nptel/106104844/5a25d4f043d7) (Video)
34. [Lecture 34: MCMC Examples](https://invisiblenetwork.ai/learn/nptel/106104844/82249ab5c271) (Video)
35. [Lecture 35: MCMC Sampling for Data with Multiple Observations](https://invisiblenetwork.ai/learn/nptel/106104844/68906340708c) (Video)
36. [Lecture 36: Hamiltonian Monte Carlo](https://invisiblenetwork.ai/learn/nptel/106104844/ef2966935217) (Video)
37. [Lecture 37: HMC Implimentation](https://invisiblenetwork.ai/learn/nptel/106104844/155dd9dcd08c) (Video)
38. [Lecture 38: HMC Example](https://invisiblenetwork.ai/learn/nptel/106104844/0e4ff3534bad) (Video)
39. [Lecture 39: Introduction to brms](https://invisiblenetwork.ai/learn/nptel/106104844/c5dffc92056d) (Video)
40. [Lecture 40: Parameter Estimation using brms](https://invisiblenetwork.ai/learn/nptel/106104844/5fccf71fd6b8) (Video)
41. [Lecture 41: Introduction to Regression Models](https://invisiblenetwork.ai/learn/nptel/106104844/8018a5858f22) (Video)
42. [Lecture 42: Regression Modeling using brms](https://invisiblenetwork.ai/learn/nptel/106104844/8d57900899aa) (Video)
43. [Lecture 43: Logistic & Poisson Regression](https://invisiblenetwork.ai/learn/nptel/106104844/68d87d9802a2) (Video)
44. [Lecture 44: Regression Modelling Examples](https://invisiblenetwork.ai/learn/nptel/106104844/ef26fe6e1e72) (Video)
45. [Lecture 45: Interaction Effect & Nested Contrast](https://invisiblenetwork.ai/learn/nptel/106104844/262771e19c7e) (Video)
46. [Lecture 46: Posterior Predictive Checks](https://invisiblenetwork.ai/learn/nptel/106104844/032f8f18407f) (Video)
47. [Lecture 47: Information- Theoretic Measures of Predictive Accuracy](https://invisiblenetwork.ai/learn/nptel/106104844/e81000d00e2f) (Video)
48. [Lecture 48: Expected Log Predictive Density (ELPD)](https://invisiblenetwork.ai/learn/nptel/106104844/2d8c01835c57) (Video)
49. [Lecture 49: Cross Validation](https://invisiblenetwork.ai/learn/nptel/106104844/edae7ccec269) (Video)
50. [Lecture 50: Cross - Validation Implementation](https://invisiblenetwork.ai/learn/nptel/106104844/16c748c84517) (Video)
51. [Lecture 51: Validations with Brms](https://invisiblenetwork.ai/learn/nptel/106104844/2f2b52cfb2c2) (Video)
52. [Lecture 52: Bayes Factor](https://invisiblenetwork.ai/learn/nptel/106104844/568e591b5bb1) (Video)
53. [Lecture 53: Bayes factor Implementation](https://invisiblenetwork.ai/learn/nptel/106104844/90da7df41732) (Video)
54. [Lecture 54: Hypothesis Testing using Bayes Factors](https://invisiblenetwork.ai/learn/nptel/106104844/44b884702c6b) (Video)
55. [Lecture 55: Computing Bayes Factor using Bridge Sampling](https://invisiblenetwork.ai/learn/nptel/106104844/748d843ba247) (Video)
56. [Feedback for Week 11](https://invisiblenetwork.ai/learn/nptel/106104844/60806697164c) (Video)
57. [Lecture 56: Bayesian Hierarchical Models](https://invisiblenetwork.ai/learn/nptel/106104844/8b03471144f0) (Video)
58. [Lecture 57: Hierarchical Regression Models](https://invisiblenetwork.ai/learn/nptel/106104844/1e2d3b000aaf) (Video)
59. [Lecture 58:Individual Difference Models](https://invisiblenetwork.ai/learn/nptel/106104844/28689caef747) (Video)
60. [Lecture 59: Hierarchical Modeling with brms](https://invisiblenetwork.ai/learn/nptel/106104844/be592a35d6ea) (Video)
61. [Lecture 60: Theory Testing & Computational Modeling](https://invisiblenetwork.ai/learn/nptel/106104844/d4b700d2ff27) (Video)