PATTERN RECOGNITION AND MACHINE LEARNING CHAPTER 2: PROBABILITY DISTRIBUTIONS Parametric Distributions Basic building blocks: Need to determine given

Representation: or ? Recall Curve Fitting Binary Variables (1) Coin flipping: heads=1, tails=0

Bernoulli Distribution Binary Variables (2) N coin flips: Binomial Distribution

Binomial Distribution Parameter Estimation (1) ML for Bernoulli Given: Parameter Estimation (2)

Example: Prediction: all future tosses will land heads up Overfitting to D Beta Distribution Distribution over

. Bayesian Bernoulli The Beta distribution provides the conjugate prior for the Bernoulli distribution.

Beta Distribution Prior Likelihood = Posterior Likelihood = Posterior Properties of the Posterior As the size of the data set, N

, increase Prediction under the Posterior What is the probability that the next coin toss will land heads up?

Multinomial Variables 1-of-K coding scheme: ML Parameter estimation Given: Ensure

, use a Lagrange multiplier, . The Multinomial Distribution The Dirichlet Distribution

Conjugate prior for the multinomial distribution. Bayesian Multinomial (1) Bayesian Multinomial (2)

The Gaussian Distribution Central Limit Theorem The distribution of the sum of N i.i.d. random variables becomes increasingly Gaussian as N grows. Example: N uniform [0,1] random variables.

Geometry of the Multivariate Gaussian Moments of the Multivariate Gaussian (1) thanks to anti-symmetry of z

Moments of the Multivariate Gaussian (2) Partitioned Gaussian Distributions Partitioned Conditionals and Marginals Partitioned Conditionals and Marginals

Bayes Theorem for Gaussian Variables Given we have where

Maximum Likelihood for the Gaussian (1) Given i.i.d. data hood function is given by Sufficient statistics , the log likeli-

Maximum Likelihood for the Gaussian (2) Set the derivative of the log likelihood function to zero, and solve to obtain Similarly

Maximum Likelihood for the Gaussian (3) Under the true distribution Hence define Sequential Estimation

Contribution of the N th data point, xN correction given xN correction weight old estimate The Robbins-Monro Algorithm (1)

Consider and z governed by p(z,) and define the regression function Seek ? such that f(?) = 0. The Robbins-Monro Algorithm (2)

Assume we are given samples from p(z,), one at the time. The Robbins-Monro Algorithm (3) Successive estimates of ? are then given by Conditions on aN for convergence :

Robbins-Monro for Maximum Likelihood (1) Regarding as a regression function, finding its root is equivalent to finding the maximum likelihood solution ML. Thus

Robbins-Monro for Maximum Likelihood (2) Example: estimate the mean of a Gaussian. The distribution of z is Gaussian with mean { ML. For the Robbins-Monro update

equation, aN = 2=N. Bayesian Inference for the Gaussian (1) Assume 2 is known. Given i.i.d. data , the likelihood function for is given by

This has a Gaussian shape as a function of (but it is not a distribution over ). Bayesian Inference for the Gaussian (2) Combined with a Gaussian prior over , this gives the posterior Completing the square over , we see that

Bayesian Inference for the Gaussian (3) where Note: Bayesian Inference for the Gaussian (4)

Example: 2 and 10. for N = 0, 1, Bayesian Inference for the Gaussian (5) Sequential Estimation

The posterior obtained after observing N { 1 data points becomes the prior when we observe the N th data point. Bayesian Inference for the Gaussian (6) Now assume is known. The likelihood

function for = 1/2 is given by This has a Gamma shape as a function of . Bayesian Inference for the Gaussian (7) The Gamma distribution

Bayesian Inference for the Gaussian (8) Now we combine a Gamma prior, , with the likelihood function for to obtain which we recognize as

with Bayesian Inference for the Gaussian (9) If both and are unknown, the joint likelihood function is given by We need a prior with the same functional

dependence on and . Bayesian Inference for the Gaussian (10) The Gaussian-gamma distribution Quadratic in . Linear in .

Gamma distribution over . Independent of . Bayesian Inference for the Gaussian (11) The Gaussian-gamma distribution

Bayesian Inference for the Gaussian (12) Multivariate conjugate priors unknown, known: p() Gaussian. unknown, known: p() Wishart, and unknown: p(,) Gaussian-Wishart,

Students t-Distribution where Infinite mixture of Gaussians. Students t-Distribution

Students t-Distribution Robustness to outliers: Gaussian vs t-distribution. Students t-Distribution The D-variate case: where

Properties: . Periodic variables Examples: calendar time, direction, We require

von Mises Distribution (1) This requirement is satisfied by where is the 0th order modified Bessel function of the

1st kind. von Mises Distribution (4) Maximum Likelihood for von Mises Given a data set, is given by

, the log likelihood function Maximizing with respect to 0 we directly obtain Similarly, maximizing with respect to m we get

which can be solved numerically for mML. Mixtures of Gaussians (1) Old Faithful data set Single Gaussian

Mixture of two Gaussians Mixtures of Gaussians (2) Combine simple models into a complex model: Component

Mixing coefficient K=3 Mixtures of Gaussians (3) Mixtures of Gaussians (4) Determining parameters , , and using

maximum log likelihood Log of a sum; no closed form maximum. Solution: use standard, iterative, numeric optimization methods or the expectation maximization algorithm (Chapter 9).

The Exponential Family (1) where is the natural parameter and so g() can be interpreted as a normalization coefficient.

The Exponential Family (2.1) The Bernoulli Distribution Comparing with the general form we see that and so Logistic sigmoid

The Exponential Family (2.2) The Bernoulli distribution can hence be written as where The Exponential Family (3.1)

The Multinomial Distribution where, , and

NOTE: The k parameters are not independent since the corresponding k must satisfy The Exponential Family (3.2) Let

. This leads to and Softmax Here the k parameters are independent. Note that

and The Exponential Family (3.3) The Multinomial distribution can then be written as where

The Exponential Family (4) The Gaussian Distribution where ML for the Exponential Family (1) From the definition of g() we get

Thus ML for the Exponential Family (2) Give a data set, function is given by

, the likelihood Thus we have Sufficient statistic Conjugate priors

For any member of the exponential family, there exists a prior Combining with the likelihood function, we get Prior corresponds to pseudo-observations with value .

Noninformative Priors (1) With little or no information available a-priori, we might choose a non-informative prior. discrete, K-nomial : 2[a,b] real and bounded: real and unbounded: improper!

A constant prior may no longer be constant after a change of variable; consider p() constant and = 2: Noninformative Priors (2) Translation invariant priors. Consider For a corresponding prior over , we have

for any A and B. Thus p() = p( { c) and p() must be constant. Noninformative Priors (3) Example: The mean of a Gaussian, ; the conjugate prior is also a Gaussian,

As , this will become constant over . Noninformative Priors (4) Scale invariant priors. Consider make the change of variable

and For a corresponding prior over , we have for any A and B. Thus p() / 1/ and so this prior is improper too. Note that this corresponds to p(ln )

being constant. Noninformative Priors (5) Example: For the variance of a Gaussian, 2, we have If = 1/2 and p() / 1/ , then p() / 1/ . We know that the conjugate distribution for is the Gamma distribution,

A noninformative prior is obtained when a0 = 0 and b0 = 0. Nonparametric Methods (1) Parametric distribution models are restricted to specific forms, which may not always be suitable; for example, consider modelling a

multimodal distribution with a single, unimodal model. Nonparametric approaches make few assumptions about the overall shape of the distribution being modelled. Nonparametric Methods (2)

Histogram methods partition the data space into distinct bins with widths i and count the number of observations, ni, in each bin. Often, the same width is

used for all bins, i = . acts as a smoothing parameter. In a D-dimensional space, using M bins in each dimension will require MD bins!

Nonparametric Methods (3) Assume observations drawn from a density p(x) and consider a small region R containing x such that If the volume of R, V, is

sufficiently small, p(x) is approximately constant over R and The probability that K out of N observations lie inside R is Bin(KjN,P ) and if N is

large Thus V small, yet K>0, therefore N large? Nonparametric Methods (4)

Kernel Density Estimation: fix V, estimate K from the data. Let R be a hypercube centred on x and define the kernel function (Parzen window) It follows that and hence

Nonparametric Methods (5) To avoid discontinuities in p(x), use a smooth kernel, e.g. a Gaussian Any kernel such that

h acts as a smoother. will work. Nonparametric Methods (6) Nearest Neighbour Density Estimation: fix K, estimate V from the data.

Consider a hypersphere centred on x and let it grow to a volume, V ?, that includes K of the given N data points. Then K acts as a smoother.

Nonparametric Methods (7) Nonparametric models (not histograms) requires storing and computing with the entire data set. Parametric models, once fitted, are much more efficient in terms of storage and computation.

K-Nearest-Neighbours for Classification (1) Given a data set with Nk data points from class Ck and , we have and correspondingly Since

, Bayes theorem gives K-Nearest-Neighbours for Classification (2) K=3

K=1 K-Nearest-Neighbours for Classification (3) K acts as a smother For , the error rate of the 1-nearest-neighbour classifier is never more than

twice the optimal error (obtained from the true conditional class distributions).