Introduction to Bayesian Methods

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Transcript of Introduction to Bayesian Methods
Introduction to Bayesian Methods Theory, Computation, Inference and Prediction
Corey ChiversPhD CandidateDepartment of BiologyMcGill University
Script to run examples in these slides can be found here:
bit.ly/Wnmb2W
These slides are here:
bit.ly/P9Xa9G
Corey Chivers, 2012
The Likelihood Principle
L x ∝P X= x 
● All information contained in data x, with respect to inference about the value of θ, is contained in the likelihood function:
Corey Chivers, 2012
The Likelihood Principle
L.J. Savage R.A. Fisher
Corey Chivers, 2012
The Likelihood Function
L x ∝P X= x 
Where θ is(are) our parameter(s) of interestex:
Attack rate
Fitness
Mean body mass
Mortality
etc...
L x = f  x
Corey Chivers, 2012
The Ecologist's Quarter Lands tails (caribou up) 60% of the time
Corey Chivers, 2012
The Ecologist's Quarter
● 1) What is the probability that I will flip tails, given that I am flipping an ecologist's quarter (p(tail=0.6))?
● 2) What is the likelihood that I am flipping an ecologist's quarter, given the flip(s) that I have observed?
Px =0.6
L=0.6  x
Corey Chivers, 2012
Lands tails (caribou up) 60% of the time
L x =∏t=1
T
∏h=1
H
1−
The Ecologist's Quarter
L=0.6  x=H T T H T
=∏t=1
3
0.6∏h=1
2
0.4
=0.03456
Corey Chivers, 2012
L x =∏t=1
T
∏h=1
H
1−
The Ecologist's Quarter
L=0.6  x=H T T H T
=∏t=1
3
0.6∏h=1
2
0.4
=0.03456
But what does this mean? 0.03456 ≠ P(θx) !!!!
Corey Chivers, 2012
How do we ask Statistical Questions?
A Frequentist asks: What is the probability of having observed data at least as extreme as my data if the null hypothesis is true?
P(data  H0) ? ← note: P=1 does not mean P(H
0)=1
A Bayesian asks: What is the probability of hypotheses given that I have observed my data?
P(H  data) ? ← note: here H denotes the space of all possible hypotheses
Corey Chivers, 2012
P(data  H0) P(H  data)
But we both want to makeinferences about our hypotheses,not the data.
Corey Chivers, 2012
Bayes Theorem
P  x=P x P
P x
● The posterior probability of θ, given our observation (x) is proportional to the likelihood times the prior probability of θ.
Corey Chivers, 2012
The Ecologist's Quarter Redux
Corey Chivers, 2012
Lands tails (caribou up) 60% of the time
L x =∏t=1
T
∏h=1
H
1−
The Ecologist's Quarter
L=0.6  x=H T T H T
=∏t=1
3
0.6∏h=1
2
0.4
=0.03456
Corey Chivers, 2012
Corey Chivers, 2012
P(x θ)
P(θ  x )But we want to know
Likelihood of data given hypothesis
● How can we make inferences about our ecologist's quarter using Bayes?
P(θ  x )=P( x θ)P(θ)
P(x )
Corey Chivers, 2012
● How can we make inferences about our ecologist's quarter using Bayes?
P  x=P x P
P x
Likelihood
Corey Chivers, 2012
● How can we make inferences about our ecologist's quarter using Bayes?
P(θ  x )=P( x θ)P(θ)
P(x )
Likelihood Prior
Corey Chivers, 2012
● How can we make inferences about our ecologist's quarter using Bayes?
P  x=P x P
P x
Likelihood Prior
Posterior
Corey Chivers, 2012
● How can we make inferences about our ecologist's quarter using Bayes?
P  x=P x P
P x
Likelihood Prior
Posterior
P x =∫P x P d
Not always a closed form solution possible!!
Corey Chivers, 2012
Randomization to Solve Difficult Problems
`
Feynman, Ulam &Von Neumann
∫ f d
Corey Chivers, 2012
(1,0 )
(0 ,1)
(0 .5 ,0 )
Monte Carlo
Throw darts at random
P(blue) = ?
P(blue) = 1/2
P(blue) ~ 7/15 ~ 1/2
Feynman, Ulam &Von Neumann
Corey Chivers, 2012
Your turn...
Let's use Monte Carlo to estimate π
 Generate random x and y values using the number sheet
 Plot those points on your graph
How many of the points fallwithin the circle?
x=4
y=17
Your turn...
Estimate π using the formula:
≈4 # in circle / total
Now using a more powerful computer!
Posterior Integration via Markov Chain Monte Carlo
A Markov Chain is a mathematical construct where given the present, the past and the future are independent.
“Where I decide to go next depends not on where I have been, or where I may go in the future – but only on where I am right now.”
Andrey Markov (maybe)
Corey Chivers, 2012
Corey Chivers, 2012
MetropolisHastings Algorithm
The Markovian Explorer!1. Pick a starting location at random.
2. Choose a new location in your vicinity.
3. Go to the new location with probability:
4. Otherwise stay where you are.
5. Repeat.
p=min 1, x proposal
xcurrent
Corey Chivers, 2012
MCMC in Action!
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● We've solved our integration problem!
P  x=P x P
P x
P  x∝Px  P
Corey Chivers, 2012
Ex: Bayesian Regression
● Regression coefficients are traditionally estimated via maximum likelihood.
● To obtain full posterior distributions, we can view the regression problem from a Bayesian perspective.
Corey Chivers, 2012
##@ 2.1 @##
Corey Chivers, 2012
Example: Salmon Regression
Y=a+bX+ϵ
ϵ ~ Normal(0,σ)
a ~Normal (0,100)
b ~Normal (0,100)
σ ~gamma (1,1/100)
Model Priors
Corey Chivers, 2012
P(a ,b ,σ X ,Y )∝P(X ,Y a ,b ,σ)
P(a)P(b)P(σ)
Example: Salmon Regression
Corey Chivers, 2012
P(X ,Y a ,b ,σ)=∏i=1
n
N ( y i ,μ=a+b x i , sd=σ)
Likelihood of the data (x,y), given the parameters (a,b,σ):
Corey Chivers, 2012
Corey Chivers, 2012
Corey Chivers, 2012
##@ 2.5 @##>## Print the Bayesian Credible Intervals> BCI(mcmc_salmon)
0.025 0.975 post_meana 13.16485 14.84092 0.9762583b 0.127730 0.455046 0.2911597Sigma 1.736082 3.186122 2.3303188
Inference:
Does body length have an effect on egg mass?EM=ab BL
Corey Chivers, 2012
The Prior revisited● What if we do have prior information?
● You have done a literature search and find that a previous study on the same salmon population found a slope of 0.6mg/cm (SE=0.1), and an intercept of 3.1mg (SE=1.2).How does this prior information change your analysis?
Corey Chivers, 2012
Corey Chivers, 2012
Example: Salmon Regression
EM=ab BL
~ Normal 0,
a ~Normal (−3.1,1 .2)
b ~Normal (0.6,0 .1)
~ gamma1,1 /100
ModelInformative
Priors
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If you can formulate the likelihood function, you can estimate the posterior, and we have a coherent way to incorporate prior information.
Corey Chivers, 2012
Most experiments do happen in a vacuum.
Making predictions using point estimates can
be a dangerous endeavor – using the posterior (aka predictive) distribution allows us to take full account of uncertainty.
Corey Chivers, 2012
How sure are we about our predictions?
Aleatory Stochasticity, randomness
Epistemic Incomplete knowledge
##@ 3.1 @##
● Suppose you have a 90cm long individual salmon, what do you predict to be the egg mass produced by this individual?
● What is the posterior probability that the egg mass produced will be greater than 35mg?
Corey Chivers, 2012
Corey Chivers, 2012
P(EM>35mg  θ)
Corey Chivers, 2012
Clark (2005)
Extensions:
Extensions:● By quantifying our uncertainty through
integration of the posterior distribution, we can make better informed decisions.
● Bayesian analysis provides the basis for decision theory.
● Bayesian analysis allows us to construct hierarchical models of arbitrary complexity.
Corey Chivers, 2012
Summary● The output of a Bayesian analysis is not a single estimate of θ, but rather the entire posterior distribution., which represents our degree of belief about the value of θ.
● To get a posterior distribution, we need to specify our prior belief about θ.
● Complex Bayesian models can be estimated using MCMC.
● The posterior can be used to make both inference about θ, and quantitative predictions with proper accounting of uncertainty.
Corey Chivers, 2012
Questions for Corey
● You can email me! [email protected]
● I blog about statistics:
bayesianbiologist.com
● I tweet about statistics:
@cjbayesian
Resources● Bayesian Updating using Gibbs Sampling
● Just Another Gibbs Sampler
● Chisquared example, done Bayesian:
http://www.mrcbsu.cam.ac.uk/bugs/winbugs/
http://madere.biol.mcgill.ca/cchivers/biol373/chisquared_done_bayesian.pdf
http://wwwice.iarc.fr/~martyn/software/jags/
Corey Chivers, 2012