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{{Approfondissements de lycée - Probabilités discrètes}}
== Introduction ==
La théorie des probabilités est l'une des théories les plus fécondes des Mathématiques. Elle traite de l'incertain et nous enseigne comment l'aborder. C'est simplement l'une des théories les plus utiles que vous rencontrerez.
Mais pas de méprise: nous n'allons pas apprendre é prédire des choses, mais plutét é utiliser des ''chances'' prédites et les rendre utiles.
<blockquote>
Therefore, we don't care, what is the probability it will rain tomorrowé, but given the probability is 60% we can make deductions, the easiest of which is the probability it will not rain tomorrow is 40%.
</blockquote>
Comme suggéré plus haut, une ''probabilité'' est un pourcentage, valant donc entre 0 et 100 % (compris). Cette valeur est directement proportionnelle à la vraisemblance qu'un événement survienne. Les mathématiciens préférent exprimer une ''probabilité'' plutôt sous la forme d'une ''proportion'', c'est-à-dire un nombre entre 0 et 1.
=== info - Pourquoi discréte ? ===
<blockquote style="padding: 1em; border: 2px dotted purple;">
Les probabilités se présentent sous deux formes: discrétes et continues. Le cas continu est considéré comme beaucoup plus difficile à comprendre, et beaucoup moins intuitif que le cas discret, dans le sens où il nécessite de solide bases en analyse (calcul intégral). Nous introduirons quelques notions utiles au cas continu un peu plus loin.
</blockquote>
== Événement et probabilité ==
En gros, un ''événement'' est quelque chose auquel on peut assigner une probabilité. Par exemple, ''la probabilité qu'il pleuve demain est 0,6'' signifie qu'à l'événement ''il pleuvra demain'', on associe une probabillité (une vraisemblance) de 0,6. On peut écrire
:P(il pleuvra demain) = 0,6.
Les mathématiciens aiment noter les événements par l'intermédiaire d'une lettre; ici, on peut choisir de noter ''A'' l'événement "il pleuvra demain". Ainsi, l'expression précédent devient
:P(A) = 0,6.
Un autre exemple: un dé honnête amménera 1, 2, 3, 4, 5 et 6 avec les mêmes chances à chaque fois qu'il sera lancé. Soit ''B'' l'événement "le dé amnénera un 1 au prochain jet"; on notera
:P(B) = 1/6
'''Mise au point'''
<blockquote style="padding: 1em; border: 2px dotted red;">
Notez que la probabilité 1/6 ne signifie pas que le dé amnénera 1 en au plus 6 essais. Sa signification précise sera discuté plus loin. Grossiérement, cela signifie juste qu'aprés un trés grand nombre de lancers, la proportion observée de 1 s'approche effectivement de la quantité 1/6.
</blockquote>
=== Événements impossible et certain ===
Two types of events are special. One type are the impossible events (e.g., the sum of digits of a two-digit number is greater than 18); the other type are certain to happen (e.g., a roll of a die will turn up 1, 2, 3, 4, 5 or 6). The probability of an impossible event is 0, while that of a certain event is 1. We write
:P(événement impossible) = 0
:P(événement certain) = 1
The above reinforces a very important principle concerning probability. Namely, the range of probability is between 0 and 1. You can '''never''' have a probability of 2.5! So remember the following
:<math>0 \leq P(E) \leq 1</math>
for all events E.
=== Complement of an event ===
A most useful concept is the '''complement''' of an event. We use :<math>\overline{B}</math>
to represent the ''event'' that ''the die will NOT turn up 1 in the next toss''. Generally, putting a bar over a variable (that represents an event) means the opposite of that event. In the above case of a die:
:<math>P(\overline{B}) = 5/6</math>
it means ''the die will turn up 2, 3, 4, 5 or 6 in the next toss has probability 5/6''. Please note that
:<math>P(\overline{E}) = 1 - P(E)</math>
for any event E.
=== Combining independent probabilities ===
It is interesting how ''independent'' probabilities can be combined to yield probabilities for more complex events. I stress the word ''independent'' here, because the following demonstrations will not work without that requirement. The exact meaning of the word will be discussed a little later on in the chapter, and we will show why ''independence'' is important in Exercise 10 of this section.
==== Adding probabilities ====
Probabilities are added together whenever an event can occur in multiple "ways." As this is a rather loose concept, the following example may be helpful. Consider rolling a single die; if we want to calculate the probability for, say, rolling an odd number, we must add up the probabilities for all the "ways" in which this can happen -- rolling a 1, 3, or 5. Consequently, we come to the following calculation:
:P(rolling an odd number) = P(rolling a 1) + P(rolling a 3) + P(rolling a 5) = 1/6 + 1/6 + 1/6 = 3/6 = 1/2 = 50%
Note that the addition of probabilities is often associated with the use of the word "or" -- whenever we say that some event E is equivalent to any of the events X, Y, '''or''' Z occurring, we use addition to combine their probabilities.
A general rule of thumb is that the probability of an event and the probability of its complement must add up to 1. This makes sense, since we intuitively believe that events, when well-defined, must either happen or not happen.
==== Multiplying probabilities ====
Probabilities are multiplied together whenever an event occurs in multiple "stages" or "steps." For example, consider rolling a single die twice; the probability of rolling a 6 both times is calculated by multiplying the probabilities for the individual steps involved. Intuitively, the first step is simply the first roll, and the second step is the second roll. Therefore, the final probability for rolling a 6 twice is as follows:
:P(rolling a 6 twice) = P(rolling a 6 the first time)<math>\times</math>P(rolling a 6 the second time) = <math>\frac{1}{6}\times\frac{1}{6}</math> = 1/36 <math>\approx</math> 2.8%
Similarly, note that the multiplication of probabilities is often associated with the use of the word "and" -- whenever we say that some event E is equivalent to '''all''' of the events X, Y, '''and''' Z occurring, we use multiplication to combine their probabilities (if they are independent).
Also, it is important to recognize that the product of multiple probabilities must be less than or equal to each of the individual probabilities, since probabilities are restricted to the range 0 through 1. This agrees with our intuitive notion that relatively complex events are usually less likely to occur.
==== Combining addition and multiplication ====
It is often necessary to use both of these operations simultaneously. Once again, consider one die being rolled twice in succession. In contrast with the previous case, we will now consider the event of rolling two numbers that add up to 3. In this case, there are clearly two steps involved, and therefore multiplication will be used, but there are also multiple ways in which the event under consideration can occur, meaning addition must be involved as well. The die could turn up 1 on the first roll and 2 on the second roll, or 2 on the first and 1 on the second. This leads to the following calculation:
:P(rolling a sum of 3) = P(1 on 1st roll)<math>\times</math>P(2 on 2nd roll) + P(2 on 1st roll)<math>\times</math>P(1 on 2nd roll) = <math>\frac{1}{6}\times\frac{1}{6}</math> + <math>\frac{1}{6}\times\frac{1}{6}</math> = 1/18 <math>\approx</math> 5.5%
This is only a simple example, and the addition and multiplication of probabilities can be used to calculate much more complex probabilities.
==== Exercises ====
Let ''A'' represent the number that turns up in a (fair) die roll, let ''C'' represent the number that turns up in a separate (fair) die roll, and let ''B'' represent a card randomly picked out of a deck:
1. A die is rolled. What is the probability of rolling a 3 i.e. calculate P(A = 3)é
2. A die is rolled. What is the probability of rolling a 2, 3, '''or''' 5 i.e. calculate P(A = 2) + P(A = 3) + P(A = 5)é
3. What is the probability of choosing a card of the suit Diamonds (in a 52-card deck)é
4. A die is rolled and a card is randomly picked from a deck of cards. What is the probability of rolling a 4 '''and''' picking the Ace of spades, i.e. calculate P(A = 4)×P(B = Ace of spades).
5. Two dice are rolled. What is the probability of getting a 1 followed by a 3é
6. Two dice are rolled. What is the probability of getting a 1 and a 3, regardless of orderé
7. Calculate the probability of rolling two numbers that add up to 7.
8. (Optional) Show the probability of ''C'' is equal to ''A'' is 1/6.
9. What is the probability that ''C'' is greater than ''A''é
10. Gareth was told that in his class 50% of the pupils play football, 30% play video games and 30% study mathematics. So if he was to choose a student from the class randomly, he calculated the probability that the student plays football, video games or studies mathematics is 50% + 30% + 30% = 1/2 + 3/10 + 3/10 = 11/10. But all probabilities should be between 0 and 1. What mistake did Gareth makeé
''' Solutions '''
1. P(A = 3) = 1/6
2. P(A = 2) + P(A = 3) + P(A = 5) = 1/6 + 1/6 + 1/6 = 1/2
3. P(B = Ace of Diamonds) + ... + P(B = King of Diamonds) = 13 é 1/52 = 1/4
4. P(A = 4) é P(B = Ace of Spades) = 1/6 é 1/52 = 1/312
5. P(A = 1) é P(A = 3) = 1/36
6. P(A = 1) é P(A = 3) + P(A = 3) é P(A = 1) = 1/36 + 1/36 = 1/18
7. Here are the possible combinations: 1 + 6 = 2 + 5 = 3 + 4 = 7. Probability of getting each of the combinations are 1/18 as in Q6. There are 3 such combinations, so the probability is 3 × 1/18 = 1/6.
9. Since both dice are fair, C > A is just as likely as C < A. So
:P(C > A) = P(C < A)
and
:P(C > A) + P(C < A) + P(A = C) = 1
But
:P(A = C) = 1/6
so P(C > A) = 5/12.
10. For example, some of those 50% who play football may also study mathematics. So we can not simply add them.
== Random Variables ==
A ''random experiment'', such as ''throwing a die'' or ''tossing a coin'', is a process that produces some uncertain outcome. We also require that a random experiments can be repeated easily. In this section we shall start using a capital letter to represent the outcome of a random experiment. For example, let ''D'' be the outcome of a die roll, ''D'' could take the value 1, 2, 3, 4, 5 or 6, but it is uncertain. We say ''D'' is a ''random variable''. Suppose now I throw a die, and it turns up 5, we say the ''observed value'' of ''D'' is 5.
A random variable is simply the outcome of a certain random experiment. It is usually denoted by a CAPITAL letter, but its observed value is not. For example let
:<math>D_1, D_2, ..., D_n</math>
denote the outcome of ''n'' die throws, then we usually use
:<math>d_1, d_2, ..., d_n</math>
to denoted the observed values of each of D<sub>i</sub>'s.
From here on, random variable may be abbreviated as simply rv (a common abbreviation in other probability literatures).
=== The Bernoulli ===
This section is optional and it assumes knowledge of binomial expansion.
A Bernoulli experiment is basically a "coin-toss". If we toss a coin, we will expect to get a head or a tail equally probably. A Bernoulli experiment is slightly more versatile than that, in that the two possible outcomes need not have the same probability.
In a Bernoulli experiment you will either get a
:''success'', denoted by 1, with proability ''p'' (where ''p'' is a number between 0 and 1)
or a
:''failure'', denoted by 0, with probaility 1 - ''p''.
If the random variable ''B'' is the outcome of a Bernoulli experiment, and the probability of getting a 1 is ''p'', we say ''B'' comes from a ''Bernoulli distribution'' with success probability ''p'' and we write:
:<math>B \sim Ber(p) </math>
For example, if
:<math>C \sim Ber(0.65) </math>
then
:P(C = 1) = 0.65
and
:P(C = 0) = 1 - 0.65 = 0.35
=== Binomial Distribution ===
Suppose we want to repeat the Bernoulli experiment ''n'' times, then we get a binomial distribution. For example:
:<math>C_i \sim Ber(p)</math>
for i = 1, 2, ... , n. That is, there are ''n'' variables C<sub>1</sub>, C<sub>2</sub>, ... , C<sub>n</sub> and they all come from the same Bernoulli distribution. We consider:
:<math>B = C_1 + C_2 + ... + C_n</math>
, then ''B'' is simply the rv that counts the number of successes in ''n'' trials (experiments). Such a variables is called a binomial variable, and we write
:<math>B \sim B(n,p)</math>
''' Example 1 '''
Aditya, Gareth, and John are equally able. Their probability of scoring 100 in an exam follows a Bernoulli distribution with success probability 0.9. What is the probability of
:i) One of them getting 100é
:ii) Two of them getting 100é
:iii) All 3 getting 100é
:iv) None getting 100é
''' Solution '''
We are dealing with a binomial variable, which we will call ''B''. And
:<math>B \sim Bin(3,0.9)</math>
i) We want to calculate
:<math>P(B = 1)</math>
The probability of any of them getting 100 (success) and the other two getting below 100 (failure) is
:<math>0.9 \times 0.1 \times 0.1 = 0.009</math>
but there are 3 possible candidates for getting 100 so
:<math>P(B = 1) = 3\times 0.009 = 0.027</math>
ii) We want to calculate
:<math>P(B = 2)</math>
The probability is
:<math>0.9 \times 0.9 \times 0.1 = 0.081</math>
but there are <math>{3\choose 2} </math> combinations of candidates for getting 100, so
:<math>P(B = 2) = {3\choose 2} \times 0.081 = 0.243</math>
iii) To calculate
:<math>P(B = 3) = 0.9 \times 0.9 \times 0.9 = 0.729</math>
iv) The probability of "None getting 100" is getting 0 success, so
:<math>P(B = 0) = 0.1 \times 0.1 \times 0.1 = 0.001</math>
The above example strongly hints at the fact the binomial distribution is connected with the binomial expansion. The following result regarding the binomial distribution is provided without proof, the reader is encouraged to check its correctness.
If
:<math>B \sim Bin(n,p)</math>
then
:<math>P(B = k) = {n \choose k} p^k (1-p)^{n-k}</math>
This is the ''k''th term of the binomial expansion of (''p'' + ''q'')<sup>n</sup>, where ''q'' = 1 - ''p''.
''' Exercises '''
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=== Distribution ===
...
=== Events ===
In the previous sections, we have slightly abused the use of the word event. An event should be thought of as a collection of random outcomes of a certain rv.
Let us introduce some notations first. Let ''A'' and ''B'' be two events, we define
:<math>\, A \cap B </math>
to be the event of ''A and B''. We also define
:<math> A \cup B </math>
to be the event of ''A or B''. As demonstrated in exercise 10 above,
:<math>\, P(A \cup B) \ne P(A) + P(B)</math>
in general.
Let's see some examples. Let ''A'' be the event of getting a number less than or equal to 4 when tossing a die, and let ''B'' be the event of getting an odd number. Now
:P(''A'') = 2/3
and
:P(''B'') = 1/2
but the probability of ''A or B'' does not equal to the sum of the probabilities, as below
:<math>P(A \cup B) \ne P(A) + P(B) = \frac{1}{2} + \frac{2}{3} = \frac{7}{6}</math>
as 7/6 is greater than 1.
It is not difficult to see that the event of throwing a 1 or 3 is included in both ''A'' and ''B''. So if we simply add P(''A'') and P(''B''), some events' probabilities are being added twice!
The Venn diagram below should clarify the situation a little more,
::[[image:HSE_Venn1.png|A or B]]
think of the blue square as the probability of ''B'' and the yellow square as the probability of ''A''. These two probabilities overlap, and where they do is the probability of ''A and B''. So the probability of ''A or B'' should be:
:<math>P(A \cup B) = P(A) + P(B) - P(A \cap B)</math>
The above formula is called the ''Simple Inclusion Exclusion Formula''.
If for events ''A'' and ''B'', we have
:<math>P(A \cap B) = 0</math>
we say ''A'' and ''B'' are '''disjoint'''. The word means ''to separate''. If two events are disjoint we have the following Venn diagram representing them:
::[[image:HSE_Venn_2.png|A and B are disjoint]]
==== info -- Venn Diagram ====
<blockquote style="padding: 1em; border: 2px dotted purple;">
Traditionally, Venn Diagrams are used to illustrate sets graphically. A set being simply a collection of things, e.g. {1, 2, 3} is a set consisting of 1, 2 and 3. Note that Venn diagrams are usually drawn round. It is generally very difficult to draw Venn diagrams for more than 3 intersecting sets. E.g. below is a Venn diagram showing four intersecting sets:
<center>[[image:Venn-four.png|Four intersecting sets]]</center>
</blockquote>
=== Expectation ===
The expectation of a random variable can be roughly thought of as the long term average of the outcome of a certain repeatable random experiment. By long term average it is meant that if we perform the underlying experiment many times and average the outcomes. For example, let ''D'' be as above, the observed values of ''D'' (1,2 ... or 6) are equally likely to occur. So if you were to toss the die a large number of times, you would expect each of the numbers to turn up roughly an equal number of times. So the expectation is
:<math>\frac{1 + 2 + 3 + 4 + 5 + 6}{6} = 3.5</math>
. We denote the expection of ''D'' by E(''D''), so
:<math> E(D) = 3.5 </math>
We should now properly define the expectation.
Consider a random variable ''R'', and suppose the possible values it can take are r<sub>1</sub>, r<sub>2</sub>, r<sub>3</sub>, ... , r<sub>n</sub>. We define the expectation to be
:<math>E(R) = r_1P(R = r_1) + r_2P(R = r_2) + ... + r_nP(R = r_n)</math>
'''Think about it:''' Taking into account the expectation is the long term average of the outcomes. Can you explain why is E(''R'') defined the way it isé
''' Example 1 '''
In a fair coin toss, let 1 represent tossing a head and 0 a tail. The same coin is tossed 8 times. Let ''C'' be a random variable representing the number of heads in 8 tossesé What is the expectation of ''C'', i.e. calculate E(''C'')é
''' Solution 1 '''
...
''' Solution 2 '''
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== Areas as probability ==
The uniform distributions.
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........
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== Order Statistics ==
Estimate the x in U[0, x].
...
== Addition of the Uniform distribution ==
Adding U[0,1]'s and introduce the CLT.
....
'' to be continued ...''
{{High_School_Mathematics_Extensions/Suggestions}}
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