lec-33.handout - Administration Midterm regrades due by...

Info iconThis preview shows pages 1–5. Sign up to view the full content.

View Full Document Right Arrow Icon

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
This is the end of the preview. Sign up to access the rest of the document.

Unformatted text preview: Administration Midterm regrades due by November 28th after lecture. CS70: Satish Rao: Lecture 33. Continuous Probability 1. Normal (Gaussian) Distribution. 2. Joint distributions. 3. Buffons needle. 4. Begin inference. Normal Distribution. For any and , a normal random variable has pdf f ( X ) = 1 2 2 e- ( x- ) 2 / 2 2 . Standard normal has = and = 1 .- 2 2 . 2 . 4 Also is Gaussian distribution. Central limit theorem. Law of Large Numbers: For any set of independent identically distributed random variables, X i , A n = 1 n X i tends to the mean. Say X i have expecation = E ( X i ) and variance 2 . Mean of A n is , and variance is 2 n . Let A n = A n- / n . E ( A n ) = 1 / n ( E ( A n )- ) = . Var ( A n ) = 1 2 / n Var ( A n ) = 1 . Central limit theorem: As n goes to infinity the distribution of A n approaches the standard normal distribution....
View Full Document

This note was uploaded on 02/29/2012 for the course COMPSCI 70 taught by Professor Rau during the Fall '11 term at University of California, Berkeley.

Page1 / 16

lec-33.handout - Administration Midterm regrades due by...

This preview shows document pages 1 - 5. Sign up to view the full document.

View Full Document Right Arrow Icon
Ask a homework question - tutors are online