Statistics > Study Notes > Lecture11 Chapter 7: Estimation_duke_university_STAT 611 (All)
Lecture11 Chapter 7: Estimation_duke_university_STAT 611
Document Content and Description Below
Chapter 7: Estimation Sections 7.1 Statistical Inference Bayesian Methods: 7.2 Prior and Posterior Distributions 7.3 Conjugate Prior Distributions 7.4 Bayes Estimators Frequentist Methods: 7.5 ... Maximum Likelihood Estimators 7.6 Properties of Maximum Likelihood Estimators Skip: p. 434-441 (EM algorithm and Sampling Plans) 7.7 Sufficient Statistics Skip: 7.8 Jointly Sufficient Statistics Skip: 7.9 Improving an Estimator STA 611 (Lecture 11) Expectation Oct 4, 2012 1 / 23 Chapter 7 7.1 Statistical Inference Statistical Inference We have seen statistical models in the form of probability distributions: f(x|θ) In this section the general notation for any parameter will be θ The parameter space will be denoted by Ω For example: Life time of a christmas light series follows the Expo(θ) The average of 63 poured drinks is approximately normal with mean θ The number of people that have a disease out of a group of N people follows the Binomial(N, θ) distribution. In practice the value of the parameter θ is unknown [Show More]
Last updated: 3 years ago
Preview 1 out of 23 pages
Buy this document to get the full access instantly
Instant Download Access after purchase
Buy NowInstant download
We Accept:
Reviews( 0 )
$20.00
Can't find what you want? Try our AI powered Search
Document information
Connected school, study & course
About the document
Uploaded On
Aug 30, 2021
Number of pages
23
Written in
All
Additional information
This document has been written for:
Uploaded
Aug 30, 2021
Downloads
0
Views
132
