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04_Fitting_Probability_Models
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Fitting probability distributions --Maximum likelihood --Maximum a posteriori --Bayesian approach

1.Computer vision: models, learning and inference Chapter 4 Fitting Probability Models

2.Structure 2 2 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince Fitting probability distributions Maximum likelihood Maximum a posteriori Bayesian approach Worked example 1: Normal distribution Worked example 2: Categorical distribution

3.Fitting: As the name suggests: find the parameters under which the data are most likely: Maximum Likelihood 3 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince Predictive Density: Evaluate new data point under probability distribution with best parameters We have assumed that data was independent (hence product)

4.Maximum a posteriori (MAP) Fitting As the name suggests we find the parameters which maximize the posterior probability . 4 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince Again we have assumed that data was independent

5.Maximum a posteriori (MAP) Fitting As the name suggests we find the parameters which maximize the posterior probability . Since the denominator doesn’t depend on the parameters we can instead maximize 5 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince

6.Maximum a posteriori (MAP) Fitting As the name suggests we find the parameters which maximize the posterior probability . 6 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince Since the denominator doesn’t depend on the parameters we can instead maximize

7.Maximum a posteriori (MAP) Predictive Density: Evaluate new data point under probability distribution with MAP parameters 7 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince

8.Bayesian Approach Fitting Compute the posterior distribution over possible parameter values using Bayes ’ rule: Principle: why pick one set of parameters? There are many values that could have explained the data. Try to capture all of the possibilities 8 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince

9.Bayesian Approach Predictive Density Each possible parameter value makes a prediction Some parameters more probable than others Make a prediction that is an infinite weighted sum (integral) of the predictions for each parameter value, where weights are the probabilities 9 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince

10.Predictive densities for 3 methods Maximum a posteriori: Evaluate new data point under probability distribution with MAP parameters Maximum likelihood: Evaluate new data point under probability distribution with ML parameters Bayesian: Calculate weighted sum of predictions from all possible values of parameters 10 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince

11.How to rationalize different forms? Consider ML and MAP estimates as probability distributions with zero probability everywhere except at estimate (i.e. delta functions) Predictive densities for 3 methods 11 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince

12.Structure 12 12 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince Fitting probability distributions Maximum likelihood Maximum a posteriori Bayesian approach Worked example 1: Normal distribution Worked example 2: Categorical distribution

13.Univariate Normal Distribution For short we write: Univariate normal distribution describes single continuous variable. Takes 2 parameters m and s 2 &gt;0 13 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince

14.Normal Inverse Gamma Distribution Defined on 2 variables m and s 2 &gt;0 o r for short Four parameters a,b,g &gt; 0 and d. 14 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince

15.Ready? Approach the same problem 3 different ways: Learn ML parameters Learn MAP parameters Learn Bayesian distribution of parameters Will we get the same results? 15 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince

16.As the name suggests we find the parameters under which the data is most likely. Fitting normal distribution: ML 16 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince Likelihood given by pdf

17.Fitting normal distribution: ML 17 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince

18.Fitting a normal distribution: ML Plotted surface of likelihoods as a function of possible parameter values ML Solution is at peak 18 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince

19.Fitting normal distribution: ML Algebraically: or alternatively, we can maximize the logarithm 19 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince where:

20.Why the logarithm? The logarithm is a monotonic transformation. Hence, the position of the peak stays in the same place But the log likelihood is easier to work with 20 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince

21.Fitting normal distribution: ML How to maximize a function? Take derivative and equate to zero. 21 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince Solution:

22.Fitting normal distribution: ML Maximum likelihood solution: Should look familiar! 22 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince

23.Computer vision: models, learning and inference. ©2011 Simon J.D. Prince Least Squares 23 23 Maximum likelihood for the normal distribution... ...gives `least squares’ fitting criterion.

24.Fitting normal distribution: MAP Fitting As the name suggests we find the parameters which maximize the posterior probability .. Likelihood is normal PDF 24 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince

25.Fitting normal distribution: MAP Prior Use conjugate prior, normal scaled inverse gamma. 25 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince

26.Fitting normal distribution: MAP Likelihood Prior Posterior 26 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince

27.Fitting normal distribution: MAP Again maximize the log – does not change position of maximum 27 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince

28.Fitting normal distribution: MAP MAP solution: Mean can be rewritten as weighted sum of data mean and prior mean: 28 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince

29.Fitting normal distribution: MAP 50 data points 5 data points 1 data points

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