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STAT3120
Applied Bayesian Methods
Semester 2, 2013
Dr Frank Tuyl / Dr Darfiana Nur
School of Mathematical and Physical Sciences
The University of Newcastle, Australia
Welcome
2
Lecturers:
Dr Frank Tuyl (Course Coordinator, Weeks 7-12)
[email protected]
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STAT3120 APPLIED BAYESIAN METHODS
Lab Exercises 8 Solutions
Q1 In this question we will illustrate that all cdf function
R Reference Card
by Tom Short, EPRI PEAC, [email protected] 2004-11-07
Granted to the public domain. See www.Rpad.org for the source and latest
version. Includes material from R for Beginners by Emmanuel Paradis (with
permission).
Getting help
Most R f
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STAT3120 APPLIED BAYESIAN METHODS
Lab Exercises 10 Solutions
Q1 Consider the case where we wish to simulate from f~N(0,1
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STAT3120 APPLIED BAYESIAN METHODS
Lab Exercises 9 Solutions
Q1
If you want WinBUGS for your home PC
WinBUGS was develope
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STAT3120 APPLIED BAYESIAN METHODS
Lab Exercises 9
Q1 Introduction to WINBUGS (Gibbs Sampler).
See Tutorial Introduction
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STAT3120 APPLIED BAYESIAN METHODS
Lab Exercises 11
Q1 Revisiting the binomial regression example in Lecture 8.
Log dosag
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STAT3120 APPLIED BAYESIAN METHODS
Lab Exercises 8
Q1
In this question we will illustrate that all cdf functions really a
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STAT3120 APPLIED BAYESIAN METHODS
Lab Exercises 10
Q1 Accept-Reject algorithm
Consider the case where we wish to simulat
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STAT3120 APPLIED BAYESIAN METHODS
Lab Exercises 7 Solutions
Q1 Consider the following 6 samples of counts of rejections
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STAT3120 APPLIED BAYESIAN METHODS
Lab Exercises 7
Q1 Consider the following 6 samples of counts of rejections after hear
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STAT3120 APPLIED BAYESIAN METHODS
Lab Exercises 6 Solutions
Q1 The table below is taken from BDA. It contains results of
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STAT3120 Applied Bayesian Methods
Solutions to Lab 2
Q1 Albert, 1997, Chapter 3.4, Exercise 5: A geneticist is investiga
FACULTY OF SCIENCE AND INFORMATION
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STAT3120 APPLIED BAYESIAN METHODS
Lab Exercises 7
Q1 Consider the following 6 samples of counts of rejections after hear
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STAT3120 APPLIED BAYESIAN METHODS
Lab Exercises 3 Solutions
Q1 We looked at this question last week. Use Monte Carlo sim
FACULTY OF SCIENCE AND INFORMATION
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STAT3120 APPLIED BAYESIAN METHODS
Lab 1 Introduction to Bayesian thinking
Introduction to R (Jim Albert, Bayesian Comput
FACULTY OF SCIENCE AND INFORMATION
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STAT 3120 APPLIED BAYESIAN METHODS
Lab 6
Q1 The table below is taken from BDA, chapter 3, exercise 6. It contains result
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STAT3120 APPLIED BAYESIAN METHODS
Lab Exercises 5 Solutions
Q1
(a) The variance is known.
Suppose that we have a sample
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STAT 3120 APPLIED BAYESIAN METHODS
Lab 5
Q1 Suppose that we have a sample of size n, independently generated from a N(,2
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STAT3120-APPLIED BAYESIAN METHODS
Lab Exercises 4 Solutions
Q1
(i) and (ii)
1 ( xi ) 2
exp
f ( x1 , x 2 ,., x n | , )
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STAT3120 - APPLIED BAYESIAN METHODS
Lab 4
Q1 [Classical method revision]
If x1, x2, xn are a random sample from a popula
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STAT3120 Applied Bayesian Methods
Solutions to Lab 1
Open up R on your computer.
Introduction to R (Jim Albert, Bayesian
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STAT3120 APPLIED BAYESIAN METHODS
Lab 3 - Monte Carlo inference and integration
Q1 We looked at this question last week.
FACULTY OF SCIENCE AND INFORMATION TECHNOLOGY
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STAT3120 APPLIED BAYESIAN METHODS
Lab 2
Q1 Albert, 1997, Chapter 3.4, Exercise 5: A geneticist is investigating the link
FACULTY OF SCIENCE AND INFORMATION
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STAT3120 APPLIED BAYESIAN METHODS
LECTURE 11
11.1 Advanced MCMC with examples
In this lecture we will use some MCMC tech
FACULTY OF SCIENCE AND INFORMATION TECHNOLOGY
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STAT3120 APPLIED BAYESIAN METHODS
Semester 2, 2013
AIMS of LECTURE 10
Accept-reject algorithm (Lecture 9)
The Metropolis
LECTURE 8
Linear Regression
Linear regression is one of the most popular models utilised by quantitative data
analysts today.
Consider the ordinary linear regression model;
y X e ; e ~ N (0, 2 I )
where
o there are potentially q regressors in the model; q
STAT3120 APPLIED BAYESIAN METHODS
Semester 2, 2013
AIMS of LECTURE 11
Bioassay example (Lecture 11)
Combination MCMC and The M-H algorithm (We did this in Lecture 10)
The Metropolis algorithm (Lecture 11)
The Metropolis-Hastings (M-H) algorithm (Lecture