1. Stat310 Inference
Hadley Wickham
Tuesday, 31 March 2009
2. 1. Homework / Take home exam
2. Recap
3. Data vs. distributions
4. Estimation
1. Maximum likelihood
2. Method of moments
5. Feedback
Tuesday, 31 March 2009
3. Assessment
Short homework this week. (But you
have to do some reading)
Take home test will be available online
next Thursday.
Both take home and homework will be
due in class on Thursday April 9.
Will put up study guide asap.
Tuesday, 31 March 2009
4. Recap
What are the 5 parameters of the bivariate
normal?
If X and Y are bivariate normal, and their
correlation is zero, what does that imply
about X and Y? Is that usually true?
Tuesday, 31 March 2009
5. Data vs. Distributions
Random experiments produce data.
A repeatable random experiment has
some underlying distribution.
We want to go from the data to say
something about the underlying
distribution.
Tuesday, 31 March 2009
6. Coin tossing
Half the class generates 100 heads and tails
by flipping coins.
The other half generates 100 heads and tails
just by writing down what they think the
sequence would be.
Write up on the board.
I’ll come in and guess which group was
which.
Tuesday, 31 March 2009
7. Problem
Have some data
and a probability model, with unknown
parameters.
Want to estimate the value of those
parameters
Tuesday, 31 March 2009
8. Some definitons
Parameter space: set of all possible
parameter values
Estimator: process/function which takes
data and gives best guess for parameter
(usually many possible estimators for a
problem)
Point estimate: estimator for a single value
Tuesday, 31 March 2009
9. Example
Data: 5.7 3.0 5.7 4.5 6.0 6.3 4.9 5.8 4.4 5.8
Model: Normal(?, 1)
What is the mean of the underlying
distribution? (5.2?)
Tuesday, 31 March 2009
10. Uncertainty
Also want to be able to quantify how
certain/confident we are in our answer.
How close is our estimate to the true
mean?
Tuesday, 31 March 2009
11. Simulation
One approach to find the answer is to use
simulation, i.e., set up a case where we
know what the true answer is and see
what happens.
X ~ Normal(5, 1)
Draw 10 numbers from this distribution
and calculate their average.
Tuesday, 31 March 2009
13. Repeat 1000 times
120
100
80
count
60
40
95% of values
lie between
20
4.5 and 5.6
0
4.0 4.5 5.0 5.5 6.0
samp
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14. Theory
From Tuesday, we know what the
distribution of the average is. Write it
down.
Create a 95% confidence interval.
How does it compare to the simulation?
Tuesday, 31 March 2009
15. Why the mean?
Why is the mean of the data a good
estimate of μ? Are there other estimators
that might be as good or better?
In general, how can we figure out an
estimator for a parameter of a
distribution?
Tuesday, 31 March 2009
17. Maximum likelihood
Write down log-likelihood (i.e., given this
data how likely is it that it was generated
from this parmeter?)
Find the maximum (i.e., differentiate and
set to zero)
Tuesday, 31 March 2009
18. Example
X ~ Binomial(10, p?)
Here is some data drawn from that
random experiment: 4 5 1 5 3 2 4 2 2 4
We know the joint pdf because they are
independent. Can try out various values
of p and see which is most likely
Tuesday, 31 March 2009
19. Your turn
Write down the joint pdf for X1, X2, …, Xn
~ Binomial(n, p)
Try evaluating it for x = (4 5 1 5 3 2 4 2 2
4), n = 10, p = 0.1
Tuesday, 31 March 2009
20. Try 10 different
●
values of p
3.0e−08
2.5e−08
2.0e−08
prob
1.5e−08
1.0e−08 ●
5.0e−09
●
0.0e+00 ●
● ● ● ● ● ● ●
0.0 0.2 0.4 0.6 0.8 1.0
p
Tuesday, 31 March 2009
21. Try 100 different
values of p
3.5e−08
3.0e−08
2.5e−08
2.0e−08
prob
1.5e−08
True p is 0.3
1.0e−08
5.0e−09
0.0e+00
0.0 0.2 0.4 0.6 0.8 1.0
p
Tuesday, 31 March 2009
22. Calculus
Can do the same analytically with calculus.
Want to find the maximum of the pdf with
respect to p. (How do we do this?)
Normally call this the likelihood when
we’re thinking of the x’s being fixed and
the parameters varying.
Usually easier to work with the log pdf
(why?)
Tuesday, 31 March 2009
23. Steps
Write out log-likelihood
(Discard constants)
Differentiate and set to 0
(Check second derivative is positive)
Tuesday, 31 March 2009
24. Analytically
Mean of x’s is 3.2
n = 10
Maximum likelihood estimate of p for this
example is 0.32
Tuesday, 31 March 2009
25. Method of moments
We know how to calculate sample
moments (e.g. mean and variance of data)
We know what the moments of the
distribution are in terms of the
parameters.
Why not just match them up?
Tuesday, 31 March 2009
27. Binomial
E(X) = np Var(X) = np(1-p)
p = mean / n = 3.2 / 10 = 0.32
Tuesday, 31 March 2009
28. Binomial
E(X) = np Var(X) = np(1-p)
p = mean / n = 3.2 / 10 = 0.32
p(1-p) = var / n = 2 / 10 = 0.2
Tuesday, 31 March 2009
29. Binomial
E(X) = np Var(X) = np(1-p)
p = mean / n = 3.2 / 10 = 0.32
p(1-p) = var / n = 2 / 10 = 0.2
-p 2 + p - 0.2 = 0
Tuesday, 31 March 2009
30. Binomial
E(X) = np Var(X) = np(1-p)
p = mean / n = 3.2 / 10 = 0.32
p(1-p) = var / n = 2 / 10 = 0.2
-p 2 + p - 0.2 = 0
p = (0.276, 0.725)
Tuesday, 31 March 2009
31. Your turn
What are the method of moments
estimators for the mean and variance of
the normal distribution?
What about the gamma distribution?
Tuesday, 31 March 2009
