probability theory:

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Probability Theory: Probability theory is the branch of mathematics concerned with analysis of random phenomena. (Encyclopedia Britannica) Probability theory: “is the branch of mathematics concerned with” the study and modeling “of random phenomena.” (I added my two cents.) Agreeing with Lindley, this branch allows us to understand and quantify uncertainty. (Dennis V. Lindley. 2006. Understanding uncertainty.) Examples of uncertain phenomena.

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Probability Theory: . Probability theory is the branch of mathematics concerned with analysis of random phenomena. (Encyclopedia Britannica). - PowerPoint PPT Presentation

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Page 1: Probability Theory:

Probability Theory:

Probability theory is the branch of mathematics concerned with analysis of random phenomena. (Encyclopedia Britannica)

Probability theory: “is the branch of mathematics concerned with” the study and modeling “of random phenomena.” (I added my two cents.) Agreeing with Lindley, this branch allows us to understand and quantify uncertainty. (Dennis V. Lindley. 2006. Understanding uncertainty.)

Examples of uncertain phenomena.

Page 2: Probability Theory:

Sample Space and Events

An experiment: is any action, process or phenomenon whose outcome is subject to uncertainty (in a very loose sense!)

An outcome: is a result of an experiment.

Each trial of an experiment results in only one outcome!

A trial: is a run of an experiment

Page 3: Probability Theory:

Sample Space and Events

Examples:

1) Studying the chance of observing a head (H) or a tail (T) when flipping a coin once:

Page 4: Probability Theory:

Sample Space and Events

Some assumptions to keep in mind:

1. Experiments are assumed to be repeatable under, essentially, the same conditions. (Not always possible and not always necessary.)

2. Which outcome we will attain in a trial is uncertain, is not known before we run the experiment, but,

3. The set of all possible outcomes can be specified before performing the experiment.

Page 5: Probability Theory:

Sample Space and Events

Examples:

1) Studying the chance of observing a head (H) or a tail (T) when flipping a coin once:

Page 6: Probability Theory:

Sample Space and Events

Def. 1.2.1:A sample space: is the set of all possible outcomes, S, of

an experiment. Again, we will observe only one of these in one trial.

Def. 1.2.3:An event: is a subset of the sample space. An event

occurs when one of the outcomes that belong to it occurs.

Def. 1.2.4:An elementary (simple) event: is a subset of the sample

space that has only one outcome.

Page 7: Probability Theory:

Sample Space and Events

Examples:

1) Studying the chance of observing a head (H) or a tail (T) when flipping a coin once:

Experiment:

Set of possible outcomes (sample space), S:

Flipping a coin

{H,T}

Goal: Observe whether the coin will be H or T

Collection of possible events (Possible subsets of S):{ , {H}, {T}, {H,T}=S}

: is the empty set. S: the sure event.

Page 8: Probability Theory:

Sample Space and Events

Population Sample

Probability

We are interested in the chance that some event will occur.

Page 9: Probability Theory:

Sample Space and Events

Before observing an outcome of an experiment (i.e. before any trials) each of the possible events will have some chance of occurring.

In probability we quantify this chance and hence speculate about what an event might look like given what we might know about the experiment and the associated population.

Page 10: Probability Theory:

Sample Space and EventsExamples:

2) Studying the chance of observing a head (H) when flipping a coin once:

Experiment:

Set of possible outcomes (sample space), S:

Flipping a coin

{1, 0}, {H,T} or {S, F}

Goal: Observe a H

Collection of possible events (Possible subsets of S):{ , {1}, {0}, {1,0}=S}

Page 11: Probability Theory:

Sample Space and EventsExamples:

3) Forecasting the weather for each of the next three days on the Palouse:

Experiment:

Set of possible outcomes (sample space), S:

Observing whether the weather is rainy (R) or not (N) in each of the next three days on the Palouse.

{NNN, RNN, NRN, NNR, RRN, RNR, NRR, RRR}

Goal: Interested in whether you should bring an umbrella or not.

Page 12: Probability Theory:

Sample Space and EventsExamples:

3) Forecasting the weather the next three days on the Palouse:

Collection of possible events (Possible subsets of S):

{ , {NNN}, {RNN}, …, {NNN, RNN}, {NNN, NRN},…, {NNN, RNN, NRN},…, {NNN, RNN, NRN, NNR, RRN, RNR, NRR, RRR}=S}

{{NNN}, {RNN}, {NRN}, {NNR}, {RRN}, {RNR}, {NRR}, {RRR}} are called the elemintary events.

S, is called the sure event (as well as the sample space.)

Page 13: Probability Theory:

Sample Space and EventsExamples:

4) Forecasting the weather the next three days on the Palouse:

Experiment:

Set of possible outcomes (sample space), S:

Observing the number of rainy days of the next three days on the Palouse.

{0, 1, 2, 3}

Goal: Just want to know the number of rainy days out of those three.

Page 14: Probability Theory:

Sample Space and EventsExamples:

Some possible events (Possible subsets of S):

No rainy days will be observed = {0}

Less than one rainy days will be observe = {<1} = {0}

4) Forecasting the weather the next three days on the Palouse:

More than one rainy days will be observe = {>1} = {2, 3}

At least one rainy day will be observed = {>0} = {1, 2, 3}

Page 15: Probability Theory:

Sample Space and EventsExamples:

5) Studying the chance of observing the faces of two dice when rolled:

Experiment:

Set of possible outcomes (sample space), S:

Rolling two diceGoal: Observe faces of two dice

die1/die2 1 2 3 4 5 61 (1,1) (1,2) (1,3) (1,4) (1,5) (1,6)2 (2,1) (2,2) (2,3) (2,4) (2,5) (2,6)3 (3,1) (3,2) (3,3) (3,4) (3,5) (3,6)4 (4,1) (4,2) (4,3) (4,4) (4,5) (4,6)5 (5,1) (5,2) (5,3) (5,4) (5,5) (5,6)6 (6,1) (6,2) (6,3) (6,4) (6,5) (6,6)

Page 16: Probability Theory:

Some possible events (Possible subsets of S):

Die 1 will have face with number 2: {(2,1), (2,2), (2,3), (2,4), (2,5), (2,6)}

Sample Space and EventsExamples:

5) Studying the chance of observing the faces of two dice when rolled:

Die 2 will have face with number 3: {(1,3), (2,3), (3,3), (4,3), (5,3), (6,3)}

The simple event: Die 1 and Die 2 will have faces with numbers 3 and 4 respectively: {(3,4)}

Page 17: Probability Theory:

Sample Space and EventsExamples:

6) Studying the chance of observing the sum of faces of two dice when rolled:

Experiment:

Set of possible outcomes (sample space), S:

Rolling two diceGoal: Observe sum of faces of two dice

{2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}

Some possible events (Possible subsets of S):

Sum is less than 5: {2, 3, 4}Sum is between 3 and 9: {4, 5, 6, 7, 8}

Page 18: Probability Theory:

Sample Space and EventsExamples:

7) Studying the chance of observing a smoker and observing that that smoker has lung cancer:

Experiment:

Set of possible outcomes (sample space), S:

Observe individuals and determine whether they smoke and have lung cancer or not

Goal: Study association between smoking habits and lung cancer

{NN, SN, NC, SC}

Page 19: Probability Theory:

Sample Space and EventsExamples:

All of the above examples had a finite sample spaces; i.e. we could count the possible outcomes.

Some possible events (Possible subsets of S):

An individual is a smoker: {SN, SC}An individual does not have cancer: {NN, SN}

An individual is a non-smoker and has cancer: {NC}

7) Studying the chance of observing a smoker and then observing that that smoker has lung cancer:

Page 20: Probability Theory:

Sample Space and Events Section 2.1

Examples:

8) Flipping a coin until the first head shows up:

Experiment:

Set of possible outcomes (sample space), S:

Flipping a coin multiple times and stop when head is observed.

Goal: Observe T’s until first H

{H, TH, TTH, TTTH, TTTTH, …}

Page 21: Probability Theory:

Some possible events (Possible subsets of S):

Sample Space and EventsExamples:

8) Flipping a coin until the first head shows up:

Observing at least two tails before we stop: {TTH, TTTH, TTTTH, …}

Observing at most 4 tails before we stop: {H, TH, TTH, TTTH, TTTTH}

Observing exactly 2 tails before we stop: {TTH}

Page 22: Probability Theory:

Sample Space and EventsExamples:

9) Flipping a coin until the first head shows up:

Experiment:

Set of possible outcomes (sample space), S:

Flipping a coin multiple times and stop when head is observed.

Goal: Observe number of flips needed to stop.

{1, 2, 3, 4, 5, …}

Page 23: Probability Theory:

Sample Space and EventsExamples:

9) Flipping a coin until the first head shows up:

The above two examples had a countably infinite sample spaces; i.e. we could match the possible outcomes to the integer line.

Some possible events (Possible subsets of S):

Observing at least two tails before we stop: {3, 4, 5, 6, …}

Observing at most 4 tails before we stop: {1, 2, 3, 4, 5}

Observing exactly 2 tails before we stop: {3}

Page 24: Probability Theory:

Sample Space and Events

Def. 1.2.2:A discrete sample space: is a sample space that is either

finite or countably infinite

Page 25: Probability Theory:

Sample Space and EventsExamples:

10)Flipping a coin until the first head shows up:

Experiment:

Set of possible outcomes (sample space), S:

Flipping a coin multiple times and stop when head is observed.

Goal: Observe the time t until we stop in minutes.

Page 26: Probability Theory:

The above example has a continuous sample spaces; i.e. the possible outcomes belong to the real, number line.

Sample Space and EventsExamples:

10)Flipping a coin until the first head shows up:

Some possible events (Possible subsets of S):

t < 10: [0, 10)

: (5, 20]