Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. If you continue browsing the site, you agree to the use of cookies on this website. See our User Agreement and Privacy Policy.

Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. If you continue browsing the site, you agree to the use of cookies on this website. See our Privacy Policy and User Agreement for details.

Like this presentation? Why not share!

- Probability and statistics(assign 7... by Fatima Bianca Gueco 7960 views
- Probability and statistics(exercise... by Fatima Bianca Gueco 20886 views
- STATISTICS AND PROBABILITY (TEACHIN... by well dan 34139 views
- Probability and statistics (basic s... by Don Bosco BSIT 14131 views
- Statistics lesson 1 by Katrina Mae 11180 views
- Introduction To Statistics by albertlaporte 68655 views

1,763 views

Published on

Introduction to Probability and Statistic

No Downloads

Total views

1,763

On SlideShare

0

From Embeds

0

Number of Embeds

242

Shares

0

Downloads

117

Comments

0

Likes

8

No embeds

No notes for slide

- 1. Basic Probability and Statistic Editor: Nguyen Duc Minh Khoi Email: ducminhkhoi@gmail.com Website: https://nguyenducminhkhoi.blogspot.com
- 2. Probability: The basics
- 3. Probability: The basics (cont.) • Conditional Probability • Independence
- 4. Probability: The basics (cont.) • Rule of total Probability: • Bayes Rule: • Chain rule ii BAPBPAp | )( )|()( | yP xyPxP yxP
- 5. Probability: Random Variable (rv) – PMF vs. PDF • Discrete RV • Continuous RV
- 6. Probability: Random Variable (rv) - Cumulative Density Functions (CDF)
- 7. Probability: Expectations
- 8. Probability: Expectations (cont.) • Conditional expectation
- 9. Probability: Expectations (cont.) • Other important Values • Example:
- 10. Probability: Important discrete rv
- 11. Probability: Important continuous rv
- 12. Probability: Multiple variables
- 13. Probability: Multiple variables (Covariance & Correlation)
- 14. Law of large number & Central Limit Theorem
- 15. Statistics • Give data, how to find the model (pattern) of this data. • 2 school of thoughts: • 𝜃in Bayesian is a rv (have prior p(𝜃)); 𝜃 in Classical is unknown constant.
- 16. Classical Inference: Maximum Likelihood Estimator (MLE) Step: 1. Log; 2. derive; 3. solve for 𝜃
- 17. Classical Inference: Other methods • Linear Regression
- 18. Bayesian Inference:
- 19. Bayesian Inference (cont.)
- 20. References • http://www.stanford.edu/class/cme308/OldWebsit e/notes/chap2.pdf • http://ocw.mit.edu/courses/electrical-engineering- and-computer-science/6-041sc-probabilistic- systems-analysis-and-applied-probability-fall- 2013/resource-index/ • Bertsekas, Dimitri P. Introduction to Probability: Dimitri P. Bertsekas and John N. Tsitsiklis. Athena scientific, 2002.

No public clipboards found for this slide

×
### Save the most important slides with Clipping

Clipping is a handy way to collect and organize the most important slides from a presentation. You can keep your great finds in clipboards organized around topics.

Be the first to comment