# Bayesian parameter estimation of discriminative models

## Introduction

### Generative and discriminative models

#### Recap

For parametric models without dependent variables we have a form:

$$P(y| \theta )$$

And we have various ways of estimating $$\theta$$.

We can write this as a likelihood function:

$$L(\theta ;y )=P(y|\theta)$$

#### Discriminative models

In discriminative models we learn:

$$P(y|X, \theta )$$

Which we can write as a likelihood function:

$$L(\theta ;y, X )=P(y| X, \theta)$$

#### Generative models

In generative models we learn:

$$P(y, X| \theta )$$

Which we can write as a likelihood function:

$$L(\theta ;y, X )=P(y, X|\theta)$$

We can use the generative model to calculate dependent probabilities.

$$P(y| X, \theta )=\dfrac{P(y, X| \theta )P(\theta )}{P(X, \theta )}$$

$$P(y| X, \theta )=\dfrac{P(y, X| \theta )}{P(X| \theta )}$$

## Bayesian parameter estimation

### Bayesian parameter estimation for dependent models

#### Recap

For non-dependent models we had:

$$P(\theta |y)=\dfrac{P(y, \theta)}{P(y)}$$

$$P(\theta |y)=\dfrac{P(y| \theta)P(\theta )}{P(y)}$$

The bottom bit is a normalisation factor, and so we can use:

$$P(\theta |y)\propto P(y| \theta)P(\theta )$$

We have here:

• Our prior - $$P(\theta )$$

• Our posterior - $$P(\theta |y)$$

• Our likelihood function - $$P(y| \theta)$$

#### Bayesian regression for generative models

We know:

$$P(\theta |y,X)=\dfrac{P(y, \theta, X )}{P(y, X)}$$

$$P(\theta |y,X)=\dfrac{P(y, X |\theta )P(\theta )}{P(y, X)}$$

The bottom bit is a normalisation factor, and so we can use:

$$P(\theta |y,X)\propto P(y, X| \theta)P(\theta)$$

We have here:

• Our prior - $$P(\theta )$$

• Our posterior - $$P(\theta |y,X)$$

• Our likelihood function - $$P(y, X| \theta )$$

#### Bayesian regression for discriminative models

We know:

$$P(\theta |y,X)=\dfrac{P(y, \theta, X )}{P(y, X)}$$

$$P(\theta |y,X)=\dfrac{P(y| \theta, X )P(\theta, X)}{P(y, X)}$$

$$P(\theta |y,X)=\dfrac{P(y| \theta, X )P(\theta )P(X|\theta )}{P(y, X)}$$

We assume $$P(X|\theta )=X$$ and so:

$$P(\theta |y,X)=\dfrac{P(y| \theta, X )P(\theta )P(X)}{P(y, X)}$$

The bottom bit is a normalisation factor, and so we can use:

$$P(\theta |y,X)\propto P(y| X, \theta)P(\theta)$$

We have here:

• Our prior - $$P(\theta )$$

• Our posterior - $$P(\theta |y,X)$$

• Our likelihood function - $$P(y| X, \theta )$$

### Prior and posterior predictive distributions for dependent variables

#### Prior predictive distribution

Our prior predictive distribution for $$P(y|X)$$ depends on our prior for $$\theta$$.

$$P(y|X)=\int_\Theta P(\mathbf y|X, \theta)P(\theta )d\theta$$

#### Posterior predictive distribution

Once we have calculated $$P(\theta |\mathbf y, \mathbf X)$$, we can calculate a posterior probability distribution for $$P(y|X)$$.

$$P(y|\mathbf x, \mathbf y, \mathbf X )=\int_\Theta P(y|\mathbf x, \theta)P(\theta |\mathbf y, \mathbf X)d\theta$$