# Continuous Probability Distributions

This lesson will introduce the common probability distributions implementations in JAX.

## Probability distribution

A probability distribution is a function that gives the **probability of occurrence of a random event**. It can be either **discrete** or **continuous**, depending on the domain. We can formally define it as:

$\mathcal P: \mathcal A\to \mathcal R$

Here $\mathcal A$ can determine the distribution as either discrete or continuous as being a discrete or continuous set, respectively.

There are a couple of useful ways to describe a probability distribution:

- Cumulative Distribution Function (CDF)
- Probability Density Function (PDF)

### Cumulative Distribution Function (CDF)

The CDF of a distribution is the value it will take for $x$ less than (or equal to) the given value (generalized as $X$). Formally,

$F_X(x) = {P}(X\leq x) = \int_{-\infty}^x f_X(u) \, du$

Note:Since probabilities are always non-negative, CDF is a non-decreasing function.

### Probability Density Function (PDF)

More often, we are interested in calculating the value for a particular $x$ rather than the whole limit. Here we can use the Probability Density Function (PDF). This can be calculated by taking the derivative of the CDF:

$f_X(x) = \frac{d}{dx} F_X(x)$

## Moments

The expected value of a random variable is an intuitive measure of its **mean**, which is formally defined as:

$\mathrm{E}[X] = \sum_{i=1}^\infty x_i\, p_i$

As a simple example, suppose the probability of a dice roll is as follows:

$P(1) = P(6) = 0.1$

$P(2) = P(3)=P(4) =P(5) = 0.2$

The expected value will be:

$\mathrm{E}[X] = \sum_{i=1}^6 x_i\, p_i =0.1(1)+0.2(2)+0.2(3)+0.2(4)+0.2(5)+0.1(6) =3.5$

This notion of expected value can be extended to variance, etc., which are collectively known as **moments**.

The $\mathrm{n}^{th}$ moment can be defined as:

$\mu_n = \mathrm{E} \left[ ( X - \mathrm{E}[X] )^n \right] = \int_{-\infty}^{+\infty} (x - \mu)^n f(x)\,\mathrm{d} x$

The first four moments are:

**Mean****Variance**- Skewness
- Kurtosis

Usually, we consider only mean and variance.

Now we’ll revise some commonly used probability distributions along with their respective JAX functions.

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