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Mathematics for Informatics 4a
Jose Figueroa-O’Farrill
Lecture 118 January 2012
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Introduction
Topic: Probability and random processesLectures: AT3 on Wednesday and Friday at 12:10pmOffice hours: by appointment (email) at JCMB 6321Email: [email protected]
Jose Figueroa-O’Farrill mi4a (Probability) Lecture 1 2 / 23
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The future was uncertain
Jose Figueroa-O’Farrill mi4a (Probability) Lecture 1 3 / 23
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The future is still uncertain
Jose Figueroa-O’Farrill mi4a (Probability) Lecture 1 4 / 23
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The universe is fundamentally uncertain!
Jose Figueroa-O’Farrill mi4a (Probability) Lecture 1 5 / 23
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There is no god of Algebra, but...
there are gods of probability
Fu Lu ShouShichi FukujinLakshmiTycheFortuna
Jose Figueroa-O’Farrill mi4a (Probability) Lecture 1 6 / 23
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There is no god of Algebra, but...
there are gods of probability
Fu Lu ShouShichi FukujinLakshmiTycheFortuna
Jose Figueroa-O’Farrill mi4a (Probability) Lecture 1 6 / 23
![Page 8: Mathematics for Informatics 4ajmf/Teaching/mi4a/Lecture01-2012.01.18.pdfJan 18, 2012 · Mathematics for Informatics 4a Jose Figueroa-O’Farrill´ Lecture 1 18 January 2012 Jose](https://reader033.vdocuments.us/reader033/viewer/2022042310/5ed8321a0fa3e705ec0e0364/html5/thumbnails/8.jpg)
There is no god of Algebra, but...
there are gods of probabilityFu Lu Shou
Shichi FukujinLakshmiTycheFortuna
Jose Figueroa-O’Farrill mi4a (Probability) Lecture 1 6 / 23
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There is no god of Algebra, but...
there are gods of probabilityFu Lu ShouShichi Fukujin
LakshmiTycheFortuna
Jose Figueroa-O’Farrill mi4a (Probability) Lecture 1 6 / 23
![Page 10: Mathematics for Informatics 4ajmf/Teaching/mi4a/Lecture01-2012.01.18.pdfJan 18, 2012 · Mathematics for Informatics 4a Jose Figueroa-O’Farrill´ Lecture 1 18 January 2012 Jose](https://reader033.vdocuments.us/reader033/viewer/2022042310/5ed8321a0fa3e705ec0e0364/html5/thumbnails/10.jpg)
There is no god of Algebra, but...
there are gods of probabilityFu Lu ShouShichi FukujinLakshmi
TycheFortuna
Jose Figueroa-O’Farrill mi4a (Probability) Lecture 1 6 / 23
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There is no god of Algebra, but...
there are gods of probabilityFu Lu ShouShichi FukujinLakshmiTyche
Fortuna
Jose Figueroa-O’Farrill mi4a (Probability) Lecture 1 6 / 23
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There is no god of Algebra, but...
there are gods of probabilityFu Lu ShouShichi FukujinLakshmiTycheFortuna
Jose Figueroa-O’Farrill mi4a (Probability) Lecture 1 6 / 23
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The mathematical study of Probability
Some notable namesGerolamo Cardano (1501-1576)
Pierre de Fermat (1601-1665)Blaise Pascal (1623-1662)Christiaan Huygens (1629-1695)Jakob Bernoulli (1654-1705)Abraham de Moivre (1667-1754)Thomas Bayes (1702-1761)Pierre-Simon Laplace (1749-1827)Adrien-Marie Legendre (1752-1833)Andrei Markov (1866-1922)Andrei Kolmogorov (1903-1987)Claude Shannon (1916-2001)
Jose Figueroa-O’Farrill mi4a (Probability) Lecture 1 7 / 23
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The mathematical study of Probability
Some notable namesGerolamo Cardano (1501-1576)Pierre de Fermat (1601-1665)
Blaise Pascal (1623-1662)Christiaan Huygens (1629-1695)Jakob Bernoulli (1654-1705)Abraham de Moivre (1667-1754)Thomas Bayes (1702-1761)Pierre-Simon Laplace (1749-1827)Adrien-Marie Legendre (1752-1833)Andrei Markov (1866-1922)Andrei Kolmogorov (1903-1987)Claude Shannon (1916-2001)
Jose Figueroa-O’Farrill mi4a (Probability) Lecture 1 7 / 23
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The mathematical study of Probability
Some notable namesGerolamo Cardano (1501-1576)Pierre de Fermat (1601-1665)Blaise Pascal (1623-1662)
Christiaan Huygens (1629-1695)Jakob Bernoulli (1654-1705)Abraham de Moivre (1667-1754)Thomas Bayes (1702-1761)Pierre-Simon Laplace (1749-1827)Adrien-Marie Legendre (1752-1833)Andrei Markov (1866-1922)Andrei Kolmogorov (1903-1987)Claude Shannon (1916-2001)
Jose Figueroa-O’Farrill mi4a (Probability) Lecture 1 7 / 23
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The mathematical study of Probability
Some notable namesGerolamo Cardano (1501-1576)Pierre de Fermat (1601-1665)Blaise Pascal (1623-1662)Christiaan Huygens (1629-1695)
Jakob Bernoulli (1654-1705)Abraham de Moivre (1667-1754)Thomas Bayes (1702-1761)Pierre-Simon Laplace (1749-1827)Adrien-Marie Legendre (1752-1833)Andrei Markov (1866-1922)Andrei Kolmogorov (1903-1987)Claude Shannon (1916-2001)
Jose Figueroa-O’Farrill mi4a (Probability) Lecture 1 7 / 23
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The mathematical study of Probability
Some notable namesGerolamo Cardano (1501-1576)Pierre de Fermat (1601-1665)Blaise Pascal (1623-1662)Christiaan Huygens (1629-1695)Jakob Bernoulli (1654-1705)
Abraham de Moivre (1667-1754)Thomas Bayes (1702-1761)Pierre-Simon Laplace (1749-1827)Adrien-Marie Legendre (1752-1833)Andrei Markov (1866-1922)Andrei Kolmogorov (1903-1987)Claude Shannon (1916-2001)
Jose Figueroa-O’Farrill mi4a (Probability) Lecture 1 7 / 23
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The mathematical study of Probability
Some notable namesGerolamo Cardano (1501-1576)Pierre de Fermat (1601-1665)Blaise Pascal (1623-1662)Christiaan Huygens (1629-1695)Jakob Bernoulli (1654-1705)Abraham de Moivre (1667-1754)
Thomas Bayes (1702-1761)Pierre-Simon Laplace (1749-1827)Adrien-Marie Legendre (1752-1833)Andrei Markov (1866-1922)Andrei Kolmogorov (1903-1987)Claude Shannon (1916-2001)
Jose Figueroa-O’Farrill mi4a (Probability) Lecture 1 7 / 23
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The mathematical study of Probability
Some notable namesGerolamo Cardano (1501-1576)Pierre de Fermat (1601-1665)Blaise Pascal (1623-1662)Christiaan Huygens (1629-1695)Jakob Bernoulli (1654-1705)Abraham de Moivre (1667-1754)Thomas Bayes (1702-1761)
Pierre-Simon Laplace (1749-1827)Adrien-Marie Legendre (1752-1833)Andrei Markov (1866-1922)Andrei Kolmogorov (1903-1987)Claude Shannon (1916-2001)
Jose Figueroa-O’Farrill mi4a (Probability) Lecture 1 7 / 23
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The mathematical study of Probability
Some notable namesGerolamo Cardano (1501-1576)Pierre de Fermat (1601-1665)Blaise Pascal (1623-1662)Christiaan Huygens (1629-1695)Jakob Bernoulli (1654-1705)Abraham de Moivre (1667-1754)Thomas Bayes (1702-1761)Pierre-Simon Laplace (1749-1827)
Adrien-Marie Legendre (1752-1833)Andrei Markov (1866-1922)Andrei Kolmogorov (1903-1987)Claude Shannon (1916-2001)
Jose Figueroa-O’Farrill mi4a (Probability) Lecture 1 7 / 23
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The mathematical study of Probability
Some notable namesGerolamo Cardano (1501-1576)Pierre de Fermat (1601-1665)Blaise Pascal (1623-1662)Christiaan Huygens (1629-1695)Jakob Bernoulli (1654-1705)Abraham de Moivre (1667-1754)Thomas Bayes (1702-1761)Pierre-Simon Laplace (1749-1827)Adrien-Marie Legendre (1752-1833)
Andrei Markov (1866-1922)Andrei Kolmogorov (1903-1987)Claude Shannon (1916-2001)
Jose Figueroa-O’Farrill mi4a (Probability) Lecture 1 7 / 23
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The mathematical study of Probability
Some notable namesGerolamo Cardano (1501-1576)Pierre de Fermat (1601-1665)Blaise Pascal (1623-1662)Christiaan Huygens (1629-1695)Jakob Bernoulli (1654-1705)Abraham de Moivre (1667-1754)Thomas Bayes (1702-1761)Pierre-Simon Laplace (1749-1827)Adrien-Marie Legendre (1752-1833)Andrei Markov (1866-1922)
Andrei Kolmogorov (1903-1987)Claude Shannon (1916-2001)
Jose Figueroa-O’Farrill mi4a (Probability) Lecture 1 7 / 23
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The mathematical study of Probability
Some notable namesGerolamo Cardano (1501-1576)Pierre de Fermat (1601-1665)Blaise Pascal (1623-1662)Christiaan Huygens (1629-1695)Jakob Bernoulli (1654-1705)Abraham de Moivre (1667-1754)Thomas Bayes (1702-1761)Pierre-Simon Laplace (1749-1827)Adrien-Marie Legendre (1752-1833)Andrei Markov (1866-1922)Andrei Kolmogorov (1903-1987)
Claude Shannon (1916-2001)
Jose Figueroa-O’Farrill mi4a (Probability) Lecture 1 7 / 23
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The mathematical study of Probability
Some notable namesGerolamo Cardano (1501-1576)Pierre de Fermat (1601-1665)Blaise Pascal (1623-1662)Christiaan Huygens (1629-1695)Jakob Bernoulli (1654-1705)Abraham de Moivre (1667-1754)Thomas Bayes (1702-1761)Pierre-Simon Laplace (1749-1827)Adrien-Marie Legendre (1752-1833)Andrei Markov (1866-1922)Andrei Kolmogorov (1903-1987)Claude Shannon (1916-2001)
Jose Figueroa-O’Farrill mi4a (Probability) Lecture 1 7 / 23
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What it is all about
Mathematical probability aims to formalise everyday sentencesof the type:
“The chance of A is p”
where A is some “event” and p is some “measure” of thelikelihood of occurrence of that event.Example“There is a 20% chance of snow.”“There is 5% chance that the West Antarctic Ice Sheet willcollapse in the next 200 years.”“There is a low probability of Northern Rock having a liquidityproblem.”
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Trials and outcomes
This requires introducing some language.
Definition/NotationBy a trial (or an experiment) we mean any process which hasa well-defined set Ω of outcomes. Ω is called the samplespace.
ExampleTossing a coin: Ω = H, T .Tossing two coins: Ω = (H,H), (H, T), (T ,H), (T , T).
Example (Rolling a (6-sided) die)Ω = , , , , , .
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“Events are what we assign a probability to”
DefinitionAn event A is a subset of Ω. We say that an event A has (not)occurred if the outcome of the trial is (not) contained in A.
ExampleTossing a coin and getting a head: A = H.Tossing two coins and getting at least one head:A = (H,H), (H, T), (T ,H).Rolling a die and getting an even number: A = , ,
Rolling two dice and getting a total of 5:A = ( , ), ( , ), ( , ), ( , )
Warning (for infinite Ω)Not all subsets of Ω need be events!
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“Events are what we assign a probability to”
DefinitionAn event A is a subset of Ω. We say that an event A has (not)occurred if the outcome of the trial is (not) contained in A.
ExampleTossing a coin and getting a head: A = H.Tossing two coins and getting at least one head:A = (H,H), (H, T), (T ,H).Rolling a die and getting an even number: A = , ,
Rolling two dice and getting a total of 5:A = ( , ), ( , ), ( , ), ( , )
Warning (for infinite Ω)Not all subsets of Ω need be events!
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The language of setsLet us consider subsets of a set Ω.DefinitionThe complement of A ⊂ Ω is denoted Ac ⊂ Ω:
ω ∈ Ac ⇐⇒ ω 6∈ A
Clearly (Ac)c = A.
A
Ω
Ac
Example (The empty set)The complement of Ω is theempty set ∅:
ω 6∈ ∅ ∀ω ∈ Ω
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DefinitionThe union of A and B is denoted A ∪ B:
ω ∈ A ∪ B ⇐⇒ ω ∈ A or ω ∈ B or both
A B
Ω
A ∪ BRemarkFor all subsets A of Ω,A ∪∅ = A and A ∪Ω = Ω.
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DefinitionThe intersection of A and B is denoted A ∩ B:
ω ∈ A ∩ B ⇐⇒ ω ∈ A and ω ∈ B
If A ∩ B = ∅ we say A and B are disjoint.
A B
Ω
A ∩ BRemarkFor all subsets A of Ω,A ∩∅ = ∅ and A ∩Ω = A.
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Distributivity identities
Union and intersection obey distributive properties.
TheoremLet (Ai)i∈I be a family of subsets of Ω indexed by some indexset I and let B ⊂ Ω. Then⋃
i∈I
(B ∩Ai) = B ∩⋃i∈I
Ai
and ⋂i∈I
(B ∪Ai) = B ∪⋂i∈I
Ai
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Proof.
ω ∈⋂i∈I
(B ∪Ai) ⇐⇒ ∀i ∈ I, ω ∈ B ∪Ai
⇐⇒ ∀i ∈ I, ω ∈ B or ω ∈ Ai
⇐⇒ ω ∈ B or ω ∈ Ai ∀i ∈ I
⇐⇒ ω ∈ B or ω ∈⋂i∈I
Ai
⇐⇒ ω ∈ B ∪⋂i∈I
Ai .
The other equality is proved similarly.
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De Morgan’s TheoremUnion and intersection are “dual” under complementation.
Theorem (De Morgan’s)Let (Ai)i∈I be a family of subsets of Ω indexed by some indexset I. Then(⋃
i∈I
Ai
)c
=⋂i∈I
Aci and
(⋂i∈I
Ai
)c
=⋃i∈I
Aci
RemarkThis shows that complementation together with either union orinteresection is enough, since, e.g.,
A ∪ B = (Ac ∩ Bc)c
Jose Figueroa-O’Farrill mi4a (Probability) Lecture 1 16 / 23
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Proof.
ω ∈
(⋃i∈I
Ai
)c
⇐⇒ ω 6∈⋃i∈I
Ai
⇐⇒ ω 6∈ Ai ∀i ∈ I⇐⇒ ω ∈ Ac
i ∀i ∈ I
⇐⇒ ω ∈⋂i∈I
Aci .
The other equality if proved similarly.
Jose Figueroa-O’Farrill mi4a (Probability) Lecture 1 17 / 23
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DefinitionThe difference A \ B = A ∩ Bc and the symmetric differenceA4B = (A \ B) ∪ (B \A).
A B
Ω
A \ B
A B
Ω
A4B
RemarkNotice that A \ B = A \ (A ∩ B).
Jose Figueroa-O’Farrill mi4a (Probability) Lecture 1 18 / 23
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Probability/Set theory dictionary
Notation Set-theoretic language Probabilistic languageΩ Universe Sample space
ω ∈ Ω member of Ω outcomeA ⊂ Ω subset of Ω some outcome in A occursAc complement of A no outcome in A occursA ∩ B intersection Both A and BA ∪ B union Either A or B (or both)A \ B difference A, but not BA4B symmetric difference Either A or B, but not both∅ empty set impossible eventΩ whole universe certain event
Jose Figueroa-O’Farrill mi4a (Probability) Lecture 1 19 / 23
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Which subsets can be events?
For finite Ω, any subset can be an event.For infinite Ω, it is not always sensible to allow all subsetsto be events. (Trust me!)If A is an event, it seems reasonable that Ac is also anevent.Similarly, if A and B are events, it seems reasonable thatA ∪ B and A ∩ B should also be events.
In summary, the collection of events must be closed undercomplementation and pairwise union and intersection. Byinduction, it must also be closed under finite union andintersection: if A1, . . . ,AN are events, so should beA1 ∩A2 ∩ · · · ∩AN and A1 ∪A2 ∪ · · · ∪AN.
Jose Figueroa-O’Farrill mi4a (Probability) Lecture 1 20 / 23
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The following example, shows that this is not enough.
ExampleAlice and Bob play a game in which they toss a coin in turn.The winner is the first person to obtain H. Intuition says that thethe person who plays first has an advantage. We would like toquantify this intuition. Suppose Alice goes first. She wins if andonly if the first H turns out after an odd number of tosses. Letωi be the outcome TT · · · T︸ ︷︷ ︸
i−1
H. Then the event that Alice wins is
A = ω1,ω3,ω5, . . . , which is a disjoint union of a countablyinfinite number of events. In order to compute the likelihood ofAlice winning, it had better be the case that A is an event, soone demands that the family of events be closed undercountably infinite unions; that is, if Ai, for i = 1, 2, . . . , areevents, then so is
⋃∞i=1Ai.
Jose Figueroa-O’Farrill mi4a (Probability) Lecture 1 21 / 23
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σ-fieldsDefinitionA family F of subsets of Ω is a σ-field if
1 Ω ∈ F
2 if A1,A2, . . . ∈ F, then⋃∞
i=1Ai ∈ F
3 if A ∈ F, then Ac ∈ F
RemarkIt follows from De Morgan’s theorem that for a σ-field F, ifA1,A2, · · · ∈ F then
⋂∞i=1Ai ∈ F. Also Ω ∈ F, since Ω = ∅c.
Finally, a σ-field is closed under (symmetric) difference.
ExampleThe smallest σ-field is F = ∅,Ω. The largest is the power setof Ω (i.e., the collection of all subsets of Ω).
Jose Figueroa-O’Farrill mi4a (Probability) Lecture 1 22 / 23
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σ-fieldsDefinitionA family F of subsets of Ω is a σ-field if
1 Ω ∈ F
2 if A1,A2, . . . ∈ F, then⋃∞
i=1Ai ∈ F
3 if A ∈ F, then Ac ∈ F
RemarkIt follows from De Morgan’s theorem that for a σ-field F, ifA1,A2, · · · ∈ F then
⋂∞i=1Ai ∈ F. Also Ω ∈ F, since Ω = ∅c.
Finally, a σ-field is closed under (symmetric) difference.
ExampleThe smallest σ-field is F = ∅,Ω. The largest is the power setof Ω (i.e., the collection of all subsets of Ω).
Jose Figueroa-O’Farrill mi4a (Probability) Lecture 1 22 / 23
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σ-fieldsDefinitionA family F of subsets of Ω is a σ-field if
1 Ω ∈ F
2 if A1,A2, . . . ∈ F, then⋃∞
i=1Ai ∈ F
3 if A ∈ F, then Ac ∈ F
RemarkIt follows from De Morgan’s theorem that for a σ-field F, ifA1,A2, · · · ∈ F then
⋂∞i=1Ai ∈ F. Also Ω ∈ F, since Ω = ∅c.
Finally, a σ-field is closed under (symmetric) difference.
ExampleThe smallest σ-field is F = ∅,Ω. The largest is the power setof Ω (i.e., the collection of all subsets of Ω).
Jose Figueroa-O’Farrill mi4a (Probability) Lecture 1 22 / 23
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σ-fieldsDefinitionA family F of subsets of Ω is a σ-field if
1 Ω ∈ F
2 if A1,A2, . . . ∈ F, then⋃∞
i=1Ai ∈ F
3 if A ∈ F, then Ac ∈ F
RemarkIt follows from De Morgan’s theorem that for a σ-field F, ifA1,A2, · · · ∈ F then
⋂∞i=1Ai ∈ F. Also Ω ∈ F, since Ω = ∅c.
Finally, a σ-field is closed under (symmetric) difference.
ExampleThe smallest σ-field is F = ∅,Ω. The largest is the power setof Ω (i.e., the collection of all subsets of Ω).
Jose Figueroa-O’Farrill mi4a (Probability) Lecture 1 22 / 23
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σ-fieldsDefinitionA family F of subsets of Ω is a σ-field if
1 Ω ∈ F
2 if A1,A2, . . . ∈ F, then⋃∞
i=1Ai ∈ F
3 if A ∈ F, then Ac ∈ F
RemarkIt follows from De Morgan’s theorem that for a σ-field F, ifA1,A2, · · · ∈ F then
⋂∞i=1Ai ∈ F. Also Ω ∈ F, since Ω = ∅c.
Finally, a σ-field is closed under (symmetric) difference.
ExampleThe smallest σ-field is F = ∅,Ω. The largest is the power setof Ω (i.e., the collection of all subsets of Ω).
Jose Figueroa-O’Farrill mi4a (Probability) Lecture 1 22 / 23
![Page 45: Mathematics for Informatics 4ajmf/Teaching/mi4a/Lecture01-2012.01.18.pdfJan 18, 2012 · Mathematics for Informatics 4a Jose Figueroa-O’Farrill´ Lecture 1 18 January 2012 Jose](https://reader033.vdocuments.us/reader033/viewer/2022042310/5ed8321a0fa3e705ec0e0364/html5/thumbnails/45.jpg)
σ-fieldsDefinitionA family F of subsets of Ω is a σ-field if
1 Ω ∈ F
2 if A1,A2, . . . ∈ F, then⋃∞
i=1Ai ∈ F
3 if A ∈ F, then Ac ∈ F
RemarkIt follows from De Morgan’s theorem that for a σ-field F, ifA1,A2, · · · ∈ F then
⋂∞i=1Ai ∈ F. Also Ω ∈ F, since Ω = ∅c.
Finally, a σ-field is closed under (symmetric) difference.
ExampleThe smallest σ-field is F = ∅,Ω. The largest is the power setof Ω (i.e., the collection of all subsets of Ω).
Jose Figueroa-O’Farrill mi4a (Probability) Lecture 1 22 / 23
![Page 46: Mathematics for Informatics 4ajmf/Teaching/mi4a/Lecture01-2012.01.18.pdfJan 18, 2012 · Mathematics for Informatics 4a Jose Figueroa-O’Farrill´ Lecture 1 18 January 2012 Jose](https://reader033.vdocuments.us/reader033/viewer/2022042310/5ed8321a0fa3e705ec0e0364/html5/thumbnails/46.jpg)
Summary
With any experiment or trial we associate a pair (Ω,F),where
Ω, the sample space, is the set of all possible outcomes ofthe experiment; andF, the family of events, is a σ-field of subsets of Ω: a familyof subset of Ω containing the empty set and closed undercomplementation and countable unions.
In the next lecture we will see how to enhance the pair(Ω,F) to a “probability space” by assigning a measure oflikelihood (i.e., a “probability”) to the events in F.There is a dictionary between the languages of set theoryand of probability. In particular, we will use the set-theoreticlanguage freely.
Jose Figueroa-O’Farrill mi4a (Probability) Lecture 1 23 / 23
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Summary
With any experiment or trial we associate a pair (Ω,F),where
Ω, the sample space, is the set of all possible outcomes ofthe experiment; and
F, the family of events, is a σ-field of subsets of Ω: a familyof subset of Ω containing the empty set and closed undercomplementation and countable unions.
In the next lecture we will see how to enhance the pair(Ω,F) to a “probability space” by assigning a measure oflikelihood (i.e., a “probability”) to the events in F.There is a dictionary between the languages of set theoryand of probability. In particular, we will use the set-theoreticlanguage freely.
Jose Figueroa-O’Farrill mi4a (Probability) Lecture 1 23 / 23
![Page 48: Mathematics for Informatics 4ajmf/Teaching/mi4a/Lecture01-2012.01.18.pdfJan 18, 2012 · Mathematics for Informatics 4a Jose Figueroa-O’Farrill´ Lecture 1 18 January 2012 Jose](https://reader033.vdocuments.us/reader033/viewer/2022042310/5ed8321a0fa3e705ec0e0364/html5/thumbnails/48.jpg)
Summary
With any experiment or trial we associate a pair (Ω,F),where
Ω, the sample space, is the set of all possible outcomes ofthe experiment; andF, the family of events, is a σ-field of subsets of Ω: a familyof subset of Ω containing the empty set and closed undercomplementation and countable unions.
In the next lecture we will see how to enhance the pair(Ω,F) to a “probability space” by assigning a measure oflikelihood (i.e., a “probability”) to the events in F.There is a dictionary between the languages of set theoryand of probability. In particular, we will use the set-theoreticlanguage freely.
Jose Figueroa-O’Farrill mi4a (Probability) Lecture 1 23 / 23
![Page 49: Mathematics for Informatics 4ajmf/Teaching/mi4a/Lecture01-2012.01.18.pdfJan 18, 2012 · Mathematics for Informatics 4a Jose Figueroa-O’Farrill´ Lecture 1 18 January 2012 Jose](https://reader033.vdocuments.us/reader033/viewer/2022042310/5ed8321a0fa3e705ec0e0364/html5/thumbnails/49.jpg)
Summary
With any experiment or trial we associate a pair (Ω,F),where
Ω, the sample space, is the set of all possible outcomes ofthe experiment; andF, the family of events, is a σ-field of subsets of Ω: a familyof subset of Ω containing the empty set and closed undercomplementation and countable unions.
In the next lecture we will see how to enhance the pair(Ω,F) to a “probability space” by assigning a measure oflikelihood (i.e., a “probability”) to the events in F.
There is a dictionary between the languages of set theoryand of probability. In particular, we will use the set-theoreticlanguage freely.
Jose Figueroa-O’Farrill mi4a (Probability) Lecture 1 23 / 23
![Page 50: Mathematics for Informatics 4ajmf/Teaching/mi4a/Lecture01-2012.01.18.pdfJan 18, 2012 · Mathematics for Informatics 4a Jose Figueroa-O’Farrill´ Lecture 1 18 January 2012 Jose](https://reader033.vdocuments.us/reader033/viewer/2022042310/5ed8321a0fa3e705ec0e0364/html5/thumbnails/50.jpg)
Summary
With any experiment or trial we associate a pair (Ω,F),where
Ω, the sample space, is the set of all possible outcomes ofthe experiment; andF, the family of events, is a σ-field of subsets of Ω: a familyof subset of Ω containing the empty set and closed undercomplementation and countable unions.
In the next lecture we will see how to enhance the pair(Ω,F) to a “probability space” by assigning a measure oflikelihood (i.e., a “probability”) to the events in F.There is a dictionary between the languages of set theoryand of probability. In particular, we will use the set-theoreticlanguage freely.
Jose Figueroa-O’Farrill mi4a (Probability) Lecture 1 23 / 23