Net Profit
Purchase Cost
7,000,000.00 22,000,000.00
small Plane
Large Airplane
25,000,000.00 75,000,000.00
Quantity Purchased
1
<=
maximum Purchase
Quantity
5
3
Total Capital Purchased
250,000,000.00 <=
Total Profit
Capital Available
250,000,000.00
73,0
Temporary Empoyee
Ann
Ian
Joan
Sean
Required time per tast ( hours)
Word Processing Graphics Packets Registrations
35
41
27
40
47
45
32
51
39
56
36
43
32
51
25
46
hourly
wage
14.00
12.00
13.00
15.00
Total Cost
0
For Assignment Problem of this time, we
Chapter 4: Markov chain Monte Carlo
October 18, 2015
A short introduction to Bayesian inference
Suppose we have a statistical model f (y |), with unknown .
We aim to infer , conditional on observed data y .
Bayesian approach: specify a prior density () fo
Chapter 5: Extra topics in Monte Carlo simulation
Chapter 5: Extra topics in Monte Carlo
simulation
October 30, 2015
Chapter 5: Extra topics in Monte Carlo simulation
Control variates
Control variates
This is an another Monte Carlo method for variance red
Monte Carlo inference
Nial Friel
September 2015
Lectures:
Wednesday 3:00 4:00 (F101 Arts)
Friday 11:00 12:00 (Th N Arts)
Lecture slides
Lecture slides will be posted to Blackboard in advance of each
class.
Assesment:
You will be given some homework exerci
STAT20110: Probability Theory
Week 10 (13th November, 2014)
Learning Outcomes
On completion of this module students should understand:
Learning Outcomes
On completion of this module students should understand:
1. The origins of Probability Theory.
Learnin
STAT20110: Probability Theory
Week 6 (16th October, 2014)
Learning Outcomes
On completion of this module students should understand:
Learning Outcomes
On completion of this module students should understand:
1. The origins of Probability Theory.
Learning
STAT20110: Probability Theory
Week 9 (6th November, 2014)
Learning Outcomes
On completion of this module students should understand:
Learning Outcomes
On completion of this module students should understand:
1. The origins of Probability Theory.
Learning
STAT20110: Probability Theory
Week 7 (23rd October, 2014)
Learning Outcomes
On completion of this module students should understand:
Learning Outcomes
On completion of this module students should understand:
1. The origins of Probability Theory.
Learning
STAT20110: Probability Theory
Week 8 (30th October, 2014)
Learning Outcomes
On completion of this module students should understand:
Learning Outcomes
On completion of this module students should understand:
1. The origins of Probability Theory.
Learning
STAT20110: Probability Theory
Week 5 (9th October, 2014)
Learning Outcomes
On completion of this module students should understand:
Learning Outcomes
On completion of this module students should understand:
1. The origins of Probability Theory.
Learning O
STAT20110: Probability Theory
Learning Outcomes
On completion of this module students should understand:
Learning Outcomes
On completion of this module students should understand:
1. The origins of Probability Theory.
Learning Outcomes
On completion of th
STAT20110: Probability Theory
Learning Outcomes
On completion of this module students should understand:
Learning Outcomes
On completion of this module students should understand:
1. The origins of Probability Theory.
Learning Outcomes
On completion of th
STAT20110: Probability Theory
Learning Outcomes
On completion of this module students should understand:
Learning Outcomes
On completion of this module students should understand:
1. The origins of Probability Theory.
Learning Outcomes
On completion of th
STAT20110: Probability Theory
Learning Outcomes
On completion of this module students should understand:
Learning Outcomes
On completion of this module students should understand:
1. The origins of Probability Theory.
Learning Outcomes
On completion of th
STAT20110: Probability Theory
Learning Outcomes
On completion of this module students should understand:
Learning Outcomes
On completion of this module students should understand:
1. The origins of Probability Theory.
Learning Outcomes
On completion of th
STAT20110: Probability Theory
Learning Outcomes
On completion of this module students should understand:
Learning Outcomes
On completion of this module students should understand:
1. The origins of Probability Theory.
Learning Outcomes
On completion of th
STAT20110: Probability Theory
Learning Outcomes
On completion of this module students should understand:
Learning Outcomes
On completion of this module students should understand:
1. The origins of Probability Theory.
Learning Outcomes
On completion of th
STAT20100 Inferential Statistics
Tutorial 6
Week 8
1. The Pareto distribution is used to model extreme observations. If
X Pareto(, ), then the pdf of X is
f (x) =
=
+1
x
x
, for x and > 1.
(a) Find a method of moments estimator for (assuming that is
known
STAT20100 Inferential Statistics
Tutorial 5
Week 7
(Note: Questions 1 and 2 are Tutorial 4 Questions 3&4 (which were not covered
because of Quiz 1).)
1. Let X1 , X2 , . . . , Xn be a random sample from a geometric distribution,
X Geometric(p). Find the ML
STAT20100: Inferential Statistics
Homework 2
This homework is due by 5pm Monday, 30th March, 2015. You must submit your homework
at the School of Mathematical Sciences oce (G03 in Science Center North Building). For full
credit, you must show all your wor
STAT20100: Inferential Statistics
Homework 1
This homework is due by 5pm Monday, 16th February, 2015. You must submit your homework at the School of Mathematical Sciences oce (G03 in Science Center North Building).
For full credit, you must show all your
STAT20100 Inferential Statistics
Tutorial 4
Week 6
(Note 1: Tutorial in Week 5 was cancelled because of Science Day.
Note 2: Only Questions 1 and 2 of this Tutorial were covered in Week 6,
because of Quiz 1.)
1. Approximate the 80th and 95th percentiles o
STAT20100 Inferential Statistics
Tutorial Sheet 2
1. In the Bus Stop Example (p.15 in Lectures1and2 pdf):
(a) Using the marginal distributions found in Tutorial 1, determine if
X1 and X2 are independent.
2. Assume the bivariate random vector (X1 , X2 ) ha