  ### Ch 1: Introduction

• Bayes Theorem

$P(A \mid B) = \frac{P(B \mid A) \, P(A)}{P(B)}$

#### 1.2: Bayes Theorem

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

$P(has\ disease \mid positive\ test) = \frac{P(positive\ test \mid has\ disease) \, P(has\ disease)}{P(positive\ test)}$
• 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.

\begin{align} P(has\ disease \mid positive\ test) & = \frac{0.95 \times 0.005}{((0.995 \times 0.01) + (0.005 \times 0.95))} \\ & = 0.323 \end{align}

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.

\begin{align} P(no \ disease \mid negative\ test) & = \frac{P(negative\ test \mid no\ disease) \, P(no\ disease)}{P(negative\ test)} \\ & = \frac{0.99 \times 0.995}{((0.995 \times 0.99) + (0.005 \times 0.05))} \\ & = 0.9997 \end{align}

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.

\begin{align} f(x \mid \theta) & = \prod_{i=1}^n f\chi_i(x_i \mid \theta) \\ \end{align}