{[ promptMessage ]}

Bookmark it

{[ promptMessage ]}

131A_1_Lecture14-2_Winter_2012

# 131A_1_Lecture14-2_Winter_2012 - EE 131A Probability...

This preview shows pages 1–4. Sign up to view the full content.

UCLA EE131A (KY) 1 EE 131A Probability Professor Kung Yao Electrical Engineering Department University of California, Los Angeles Lecture 14-2

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

View Full Document
UCLA EE131A (KY) 2 Randomness of a sequence of numbers (1) Introduction - Given a sequence of numbers, how can we tell if it is random? Ex. 1. Given {4, 14, 23, 34}, can we “predict” the next integer? No, the next number is “42”, since these numbers are the North-South subway train station in NYC. Clearly, this sequence of no. is not random. Suppose we toss a coin and denote an “1” for a “head” and “0” for a “tail.” Then the i-th rv X i has the sample space S = {0, 1}. If given a sequence of 1’s and 0’s, from the toss of a coin, why do we believe the outcomes are “random?” In other words, what characterizes a “random sequence?”
UCLA EE131A (KY) 3 Randomness of a sequence of numbers (2) Given a coin toss of length n, we can find P(“1”) and P(“0”) by using P(“1”) = Number of “1” s / n, P(“0”) = Number of “0” s / n. The coin is “fair” if P(“1”) = P(“0”) = 1/2. In other words, we use the “relative frequency concept”

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

View Full Document
This is the end of the preview. Sign up to access the rest of the document.

{[ snackBarMessage ]}