IBS-09-SL RM 501 – Ranjit Goswami 1 Basic Probability

IBS-09-SL RM 501 – Ranjit Goswami

Basic Probability

Introduction

• Probability is the study of randomness and uncertainty.

• In the early days, probability was associated with games of chance (gambling).

Simple Games Involving Probability

Game: A fair die is rolled. If the result is 2, 3, or 4, you win $1; if it is 5, you win $2; but if it is 1 or 6, you lose $3.

Should you play this game?

Random Experiment

• a random experiment is a process whose outcome is uncertain.

Examples:

• Tossing a coin once or several times

• Picking a card or cards from a deck

• Measuring temperature of patients

• ...

Sample SpaceThe sample space is the set of all possible outcomes.

Simple EventsThe individual outcomes are called simple events.

EventAn event is any collectionof one or more simple events

Events & Sample Spaces

Example

Experiment: Toss a coin 3 times.

• Sample space

= {HHH, HHT, HTH, HTT, THH, THT, TTH, TTT}.

• Examples of events include

• A = {HHH, HHT,HTH, THH}

= {at least two heads}

• B = {HTT, THT,TTH}

= {exactly two tails.}

Basic Concepts (from Set Theory)

• The union of two events A and B, A B, is the event consisting of all outcomes that are either in A or in B or in both events.

• The complement of an event A, Ac, is the set of all outcomes in that are not in A.

• The intersection of two events A and B, A B, is the event consisting of all outcomes that are in both events.

• When two events A and B have no outcomes in common, they are said to be mutually exclusive, or disjoint, events.

Example

Experiment: toss a coin 10 times and the number of heads is observed.

• Let A = { 0, 2, 4, 6, 8, 10}.

• B = { 1, 3, 5, 7, 9}, C = {0, 1, 2, 3, 4, 5}.

• A B= {0, 1, …, 10} = .

• A B contains no outcomes. So A and B are mutually exclusive.

• Cc = {6, 7, 8, 9, 10}, A C = {0, 2, 4}.

• Commutative Laws: • A B = B A, A B = B A

• Associative Laws: • (A B) C = A (B C )

• (A B) C = A (B C) .

• Distributive Laws:• (A B) C = (A C) (B C)

• (A B) C = (A C) (B C)

Venn Diagram

A BA∩B

Probability

• A Probability is a number assigned to each subset (events) of a sample space .

• Probability distributions satisfy the following rules:

Axioms of Probability

• For any event A, 0 P(A) 1.

• P() =1.

• If A1, A2, … An is a partition of A, then

P(A) = P(A1) + P(A2) + ...+ P(An)

(A1, A2, … An is called a partition of A if A1 A2 … An = A and A1, A2, … An are mutually exclusive.)

Properties of Probability

• For any event A, P(Ac) = 1 - P(A).

• If A B, then P(A) P(B).

• For any two events A and B,

P(A B) = P(A) + P(B) - P(A B).

For three events, A, B, and C,

P(ABC) = P(A) + P(B) + P(C) -

P(AB) - P(AC) - P(BC) + P(AB C).

Example

• In a certain population, 10% of the people are rich, 5% are famous, and 3% are both rich and famous. A person is randomly selected from this population. What is the chance that the person is

• not rich?

• rich but not famous?

• either rich or famous?

Intuitive Development (agrees with axioms)

• Intuitively, the probability of an event a could be defined as:

Where N(a) is the number that event a happens in n trialsWhere N(a) is the number that event a happens in n trials

Here We Go Again: Not So Basic Probability

More Formal:

• is the Sample Space:• Contains all possible outcomes of an experiment

• in is a single outcome

• A in is a set of outcomes of interest

Independence

• The probability of independent events A, B and C is given by:

P(A,B,C) = P(A)P(B)P(C)

A and B are independent, if knowing that A has happened A and B are independent, if knowing that A has happened does not say anything about B happeningdoes not say anything about B happening

Bayes Theorem

• Provides a way to convert a-priori probabilities to a-posteriori probabilities:

Conditional Probability

• One of the most useful concepts!

Bayes Theorem

• Provides a way to convert a-priori probabilities to a-posteriori probabilities:

Using Partitions:

• If events Ai are mutually exclusive and partition

Random Variables

• A (scalar) random variable X is a function that maps the outcome of a random event into real scalar values

X(X())

Random Variables Distributions

• Cumulative Probability Distribution (CDF):

• Probability Density Function (PDF):Probability Density Function (PDF):

Random Distributions:

• From the two previous equations:

Uniform Distribution

• A R.V. X that is uniformly distributed between x1 and x2 has density function:

XX11 XX22

Gaussian (Normal) Distribution

• A R.V. X that is normally distributed has density function:

Statistical Characterizations

• Expectation (Mean Value, First Moment):

•Second Moment:Second Moment:

Statistical Characterizations

• Variance of X:

• Standard Deviation of X:

Mean Estimation from Samples

• Given a set of N samples from a distribution, we can estimate the mean of the distribution by:

Variance Estimation from Samples

• Given a set of N samples from a distribution, we can estimate the variance of the distribution by:

Pattern Classification

Chapter 1: Introduction to Pattern Recognition (Sections 1.1-1.6)

• Machine Perception

• An Example

• Pattern Recognition Systems

• The Design Cycle

• Learning and Adaptation

• Conclusion

Machine Perception

• Build a machine that can recognize patterns:

• Speech recognition

• Fingerprint identification

• OCR (Optical Character Recognition)

• DNA sequence identification

An Example

• “Sorting incoming Fish on a conveyor according to species using optical sensing”

Sea bass

Species

Salmon

• Problem Analysis

• Set up a camera and take some sample images to extract features

• Length

• Lightness

• Width

• Number and shape of fins

• Position of the mouth, etc…

• This is the set of all suggested features to explore for use in our classifier!

• Preprocessing

• Use a segmentation operation to isolate fishes from one another and from the background

• Information from a single fish is sent to a feature extractor whose purpose is to reduce the data by measuring certain features

• The features are passed to a classifier

• Classification

• Select the length of the fish as a possible feature for discrimination

The length is a poor feature alone!

Select the lightness as a possible feature.

• Threshold decision boundary and cost relationship

• Move our decision boundary toward smaller values of lightness in order to minimize the cost (reduce the number of sea bass that are classified salmon!)

Task of decision theory

• Adopt the lightness and add the width of the fish

Fish xT = [x1, x2]

Lightness Width

• We might add other features that are not correlated with the ones we already have. A precaution should be taken not to reduce the performance by adding such “noisy features”

• Ideally, the best decision boundary should be the one which provides an optimal performance such as in the following figure:

• However, our satisfaction is premature because the central aim of designing a classifier is to correctly classify novel input

Issue of generalization!

IBS-09-SL RM 501 – Ranjit Goswami 1 Basic Probability

Documents

barajasvictor.files.wordpress.com · VARGAS ALDRETE JESUS ALEJANDRO VILLA TORRES ANA YAQUELIN CANALES OLIVAS DANIEL EDUARDO FIRM Grupo IAS IAS IAS IBS IBS IBS IBS IBS IBS IBS IBS

RESPECT INTEGRITY TRANSPARENCY EFFICIENCY · 2020. 10. 21. · SVP- Quality Management & Regulatory Affairs Ranjit Kumar Singh (Ranjit) CPO Ranjit joined Solara in December, 2017,

Sugandha Goswami

Vishal Goswami

Mayuri Goswami Thesis

Project of Frooti Ranjit

Mr Ranjit Singh Ppt

ranjit report final

Sanatana Goswami

Goswami Tulsidas

TEMPLATE OF THE PROFORMA FOR UPLOADING …rku.ac.in/fdsr/PhD_Database.pdf28 Management HR Raisinghani Manju Chandan Dr. Ranjit Goswami Part Time 84 7/19/2012 Antecedents and consequences

Goswami TulsidasRachitDohavali Gitapress03

Ranjit Presentation

Maharaja Ranjit Singh AFPI

Cyber Crime Ranjit

Ranjit Pawar Dec 2009

Maharaja Ranjit Singh Punjabi

Dasrat goswami

Ranjit Singh Rangila

Maha Raja Ranjit Singh