- 01. Return to Bayes' Rule
- 02. Overview
- 03. Localization Posterior: Introduction
- 04. Localization Posterior Explanation and Implementation
- 05. Bayes' Rule
- 06. Bayes' Filter For Localization
- 07. Calculate Localization Posterior
- 08. Initialize Belief State
- 09. Initialize Priors Function
- 10. Solution: Initialize Priors Function
- 11. Quiz: How Much Data?
- 12. How Much Data: Explanation
- 13. Derivation Outline
- 14. Apply Bayes Rule with Additional Conditions
- 15. Bayes Rule and Law of Total Probability
- 16. Total Probability and Markov Assumption
- 17. Markov Assumption for Motion Model: Quiz
- 18. Markov Assumption for Motion Model: Explanation
- 19. After Applying Markov Assumption: Quiz
- 20. Recursive Structure
- 21. Lesson Breakpoint
- 22. Implementation Details for Motion Model
- 23. Noise in Motion Model: Quiz
- 24. Noise in Motion Model: Solution
- 25. Determine Probabilities
- 26. Motion Model Probabiity I
- 27. Motion Model Probability II
- 28. Coding the Motion Model
- 29. Solution: Coding the Motion Model
- 30. Observation Model Introduction
- 31. Markov Assumption for Observation Model
- 32. Finalize the Bayes Localization Filter
- 33. Bayes Filter Theory Summary
- 34. Observation Model Probability
- 35. Get Pseudo Ranges
- 36. Solution: Get Pseudo Ranges
- 37. Coding the Observation Model
- 38. Solution: Coding the Observation Model
- 39. Coding the Full Filter
- 40. Solution: Coding the Full Filter
- 41. Conclusion