  Ch 1: Introduction

• Bayes Theorem

1.2: Bayes Theorem

Example 1.2
• A: disease presence
• B: result test

• 95% effective when disease present
• 1% false positive
• 0.5% of population has disease

• (a) Calculate the probability that a person who tests positive actually has the disease.

There is a 32.3% chance that a person who tests positive actually has the disease.

• (b) Find the probability that a person who tests negative does not have the disease.

There is a 99.97% chance that a person who tests negative does not have the disease.

1.3: Likelihood

• Suppose that an experiment results in data x = (x1, x2, … , xn) T and we decide to model the data using a probability (density) function f(x|θ). This p(d)f describes how likely different data x are to occur given a value of the (unknown) parameter θ. However, once we have observed the data, f(x|θ) tells us how likely different values of the parameters θ are: it is then known as the likelihood function for θ. In other courses you may have seen it written as L(θ|x) or L(θ) but, whatever the notation used for the likelihood function, it is simply the joint probability (density) function of the data, f(x|θ), regarded as a function of θ rather than of x.
Example 1.5

The likelihood function for $\theta$ follows the formulation in the section introduction.