# Why probabilities?

## What do we want

• we want to predict the future
• discuss what can happen in the future
• talk about what could have happened in the past
• reason about how we got to this present

## Nature is deterministic

Nature has rules. Universal and permanent rules

Whatever happens in the future is the result of applying the rules to the current state of the universe

$\text{State}_{t+1} = F(\text{State}_t, \text{Parameters})$

We just need to follow the logic consequences

## Example

If we launch a ball, and we know the angle and speed, then we can predict where it will fall

We can launch a rocket and land in the moon

We can put a satellite to explore the Earth, find our position using GPS, and watch TV from other countries

We can build a plane that can fly and carry us to other countries

## The perfect time machine

If the world is deterministic, and we know

• all the rules
• all the parameters with infinite precision
• the current state of the world with infinite precision

then we can predict everything that will happen
and everything that has happened before

We just need to use logic

## We do not know enough

We do not know all the rules

Among the rules that we know, some

• have complex solutions. They are hard to calculate

• depend on parameters that we do not know

• give very different results when parameters change a little bit

Since we have imperfect knowledge, we must deal with degrees of certainty

How much we believe some predicate is true

We want to give a numeric value to the chances that our experiment is successful

We want to compare the chances of success versus failure

# Definitions

## Experiments

We will call experiment to any procedure generating a result which we do not knew before doing it

This include “natural experiments” and observations of the nature

## Outcomes

An experiment produces a single outcome

We do not know the outcome until we perform the experiment

If we knew the outcome before doing the experiment, we would not be doing it

It is useful to give a name to the set of all possible outcomes

We will call it $$Ω$$

## Examples of Outcomes

• Rain fall is 2mm in the last hour
• Plant height is 30cm
• Lottery winner has number 12345678

Exercise: What is $$Ω$$ in each case?

## Events

An event is a yes-no question that will be answered by the experiment

Having fever is an event. Thermometer showing 38.2°C is an outcome.

## Examples of events

• You have fever, or not
• You finish your Master’s degree, or not
• Rain falls, or not
• Plant grows
• You win the lottery
• Your experiment gives the expected outcome

## Evaluating rational beliefs

An event will become either true or false after an experiment

For example, a dice can be either 4 or not

We want to give a value to our rational belief that the event will become true after the experiment

The numeric value is called Probability

## Naive probabilities

Most people are familiar with the naive idea of Probability

$ℙ(A)=\frac{\text{Number of cases where }A\text{ is true}}{\text{Total number of cases}}$

This is a useful first approach, but it is easy to get confused

It is not obvious which are the cases

For example, if you throw a dice, what is the probability of getting a 6?

We have to be careful

## Probabilities depend on what we know

new information may change our confidence

For example, if we learn that the dice outcome is an even number, what is the probability of getting a 6?

What if we learn that the outcome is an odd number?

## Probabilities are personal

They

• reflect what we know
• represent our rational confidence on future events

They are subjective, because different subjects may have different knowledge

But they are not arbitrary.
We must use all the available information, and follow all the rules

## Notation

We will use capital letters to represent events. For example

$$A$$: The dice outcome is 6
$$B$$: The dice outcome is even

The probability of $$A$$, given that we know $$B$$ is $ℙ(A|B)$

This is called conditional probability

## All probabilities are conditional

We always evaluate probabilities based on what whe know

If the background knowledge is well known, and does not change, we sometimes write $ℙ(A)$

This is to simplify notation.
But do not forget that there is an implicit context.

## Important idea

The order is relevant $ℙ(A|Z)≠ℙ(Z|A)$ There are two events, 𝐴 and 𝑍

The one written after | is what we assume to be true

The one written before | is what we are asking for

One we know, the other we do not

## An event is a subset of outcomes

The set of all possible outcomes is often called Ω

An event 𝐴 can be seen as the subset of all outcomes that make the event true

For example, $Fever=\{Temp>37.5°C\}$

## Probabilities as Areas

It is useful to think that the probability of an event is the area in the drawing

The total area of Ω is 1

Usually we do not know the shape of 𝐴

## Probabilities depend on our knowledge

Our rational beliefs depend on our knowledge

If we represent our knowledge (or hypothesis) by 𝑍, the the probability of an event 𝐴 is written as $ℙ(A|Z)$ We read “the probability of event 𝐴, given that we know 𝑍”

For example, “the probability that we get a 4, given that the dice is symmetrical”

## Visually

Now outcomes are limited only to the 𝑍 region

We measure the area of $$ℙ(A|Z)$$ with respect to the area of 𝑍 instead of Ω

The shape of 𝑍 is often unknown

## Probability rules based on these two ideas

It has been proven that probabilities must be like this

1. A probability is a number between 0 and 1 inclusive $ℙ(A) ≥ 0\text{ and } ℙ(A)≤1$

2. The probability of an sure event is 1 $ℙ(\text{True}) = 1$

3. The probability of an impossible event is 0 $ℙ(\text{False}) = 0$

## Complex events

We are interested in non-trivial events, that are usually combinations of smaller events

For example, we may ask “what is the probability that, in a group of 𝑛 people, at least two persons have the same birthday?”

Fortunately, any complex event can be decomposed into simpler events, combined with and, or and not connectors

Exercise: decompose the birthday event into simpler ones

## Probability of not 𝐴

If the event 𝐴 becomes more and more plausible, then the opposite event not 𝐴 becomes less and less plausible

It can be shown that we always have $ℙ(\text{not } A) = 1-ℙ(A)$

## Total probability

We have $ℙ(\text{not } A) = 1-ℙ(A)$ therefore $ℙ(A) + ℙ(\text{not } A) = 1$

## Example: a coin

A coin is an experiment where $$Ω=\{"H","T"\}$$

Let’s take $$A$$ to be the outcome is “H”

$ℙ(X="H") + ℙ(X="T") = 1$

## Principle of indifference

If we do not have any reason to believe that one side of the coin has more chance than the other, then we assume that both sides have equal chances

If all alternatives are symmetric, then the probabilities are equal $ℙ(X="H")= ℙ(X="T")$ Therefore $ℙ(X="H")= ℙ(X="T")=\frac 1 2$

## Splitting an event in two

Lets consider two different events $$A$$ and $$B$$
(for instance, if X is the result of a dice, “X>3” and “X is even”)

$\underbrace{B}_{m} = B\text{ and }(A\text{ or not }A) = \underbrace{(B\text{ and }A)}_{m_1}\text{ or }\underbrace{(B\text{ and not }A)}_{m_2}$ We see that $$m=m_1+m_2$$ because, for an outcome where $$B$$ is true, we have either “$$(B\text{ and }A)$$ is true” or “$$(B\text{ and not }A)$$ is true”, but never both

## Exercise: probabilities of a dice

Show that for a fair dice the probability of each side is 1/6.