## Introduction

In this series of articles, We’ll dig deep into understanding Bayesian inference, starting from the basics.

The main idea behind Bayesian statistics is the Bayes theorem, we need to understand some concepts first.

## TL;DR

Let´s take the following poker cards as an example: Our experiment is to take two cards for the deck, one at a time, each card extraction is an event. We define as outcomes that a card is of a defined

If we introduce the card in the deck again before extracting the next one, we’ll have indepedent events, if we don’t the events will be dependent, due to the fact that the fact of removing one card from the deck will alter the probabilities of the next event.

## Concepts

Let’s illustrate these concepts with poker cards and rolling dices:

• An experiment is a well-defined action, that might result in a number of outcomes.
• Outcome: Possible result of an experiment. Each outcome is unique, and different outcomes are mutually exclusive.
• The sample space is the set of all possible outcomes.
• An event is a collection of outcomes.
• A probability is a number between 0 and 1, both included, that describes the degree of confidence that we have in a prediction, 0 is none or false and 1 is all or true.

$$P ( hearts ) = 0.25$$

The probability of an event $E$ is $P(E)$ is the number of possible outcomes of event $E$ is $n(E)$ divided by the number of outcomes of the sample space $n(S)$. $$\mathrm { P } ( \mathrm { E } ) = \frac { \mathrm { n } ( \mathrm { E } ) } { \mathrm { n } ( \mathrm { S } ) }$$

#### Mutually exclusive events

We call mutually exclusive events, to two or more events that cannot happen simultaneously. For example in Poker cards we cannot draw a card that is hearts and diamonds at the same time, but we can have a card that is 2 and diamonds. $$P ( hearts \cup diamonds ) = P ( hearts ) + P ( diamonds )$$

#### Conditional probability

A conditional probability is a probability based on some premise. I know that in my city there is a 5% chance of rain, but given that is autumn the probability raises to 25%, the premise is that it is autumn, this premise gives us additional information that allows us to have a more accurate prediction. $$P ( A \cap B ) = P ( A ) * P ( B )$$

We roll a six-sided dice, this is an experiment, we have 6 possible outcomes. If we get the number 3 by rolling the dice, we’ll say that from this event of rolling the dice we observed the outcome 3, that has a prior probability of 1/6.

#### Independent Events

We’ll say that 2 events are independent if one event doesn’t give any information about another event.

We have the following outcomes that we want to analyse: A = Outcome 3 in the first dice. B = Outcome 3 in the second dice. $$P ( A | B ) = P ( A )$$ We’ll read “The probability of observing 3 in the first dice given that we got 3 in the second dice is the observing of getting 3 in the first dice.

Rolling a second dice doesn’t give us any information about the first dice.

When two events are independent the probability of both of them occurring is:

$$P ( A \cap B ) = P ( A ) * P ( B )$$ We can generalize this to multiple events:

$$P \left[ \bigcap _ { i = 1 } ^ { n } A _ { i } \right] = \prod _ { i = 1 } ^ { n } P \left( A _ { i } \right)$$ Here A means rolling a dice, it can be the same dice or different dices.

#### Dependent Events

When two events are dependent the probability of both of them occurring is: $$\mathrm { P } ( \mathrm { A } \text { and } \mathrm { B } ) = \mathrm { P } ( \mathrm { A } ) \times \mathrm { P } ( \mathrm { B } | \mathrm { A } )$$ #### Prior and Posterior distributions

The prior probability distribution (prior), is ones beliefs about its quantity before some evidence is taken into account.

The posterior probability distribution (posterior), is the revised probability of an event occurring after some evidence has been taken into account.

We´ll see in the next article more about these probability distributions.