SlideShare a Scribd company logo
THROUGH ONE EXAMPLE………….
MADE BY:-
MAYANK
MULCHANDANI
 In probability theory and statistics, Bayes'
theorem (alternatively Bayes' law or Bayes'
rule) describes the probability of an event,
based on prior knowledge of conditions that
might be related to the event. For example,
if cancer is related to age, then, using
Bayes' theorem, a person's age can be used
to more accurately assess the probability
that they have cancer, compared to the
assessment of the probability of cancer
made without knowledge of the person's
age.
One of the many applications of Bayes'
theorem is Bayesian inference, a particular
approach to statistical inference. When
applied, the probabilities involved in Bayes'
theorem may have different probability
interpretations. With the Bayesian
probability interpretation the theorem
expresses how a subjective degree of belief
should rationally change to account for
availability of related evidence. Bayesian
inference is fundamental to Bayesian statistics.
 We have two bags contains Red & black
Balls..
A B
RED 2
BLACK 3
A
RED 3
BLACK 4
Case 1: what is the probability of get’s Red Ball
from bag A??? { bag A is already selected}
Should be written as…
P(R/A) = 2/5
Case 2: what is the probability of Red Ball
drawn from bag A???
P(A ∩ R) = P(A)P(R/A)
Probability of
Red ball and
from bag A
 Case 3: what is the probability of Red Ball???
P(R)=P(A ∩ R) + P(B ∩ R)
Probability of
getting red ball
from bag A
Probability of
getting red ball
from bag B
 Case 4: Given that red ball is drawn .what is
the probability that the Ball is from bag A ???
 P(A/R)=
P(A ∩ R)
P(A ∩ R) + P(B ∩ R)
 Putting
P(A ∩ R) = P(A)P(R/A)
P(R)=P(A ∩ R) + P(B ∩ R)
So…
P(A/R)=
P(A)P(R/A)
P(A ∩ R) + P(B ∩ R)
Bays theorem
Made by:-
MAYANK MULCHANDANI

More Related Content

What's hot

Bayesian networks
Bayesian networksBayesian networks
Bayesian networks
Massimiliano Patacchiola
 
Ridge regression
Ridge regressionRidge regression
Ridge regression
Ananda Swarup
 
Introduction to Maximum Likelihood Estimator
Introduction to Maximum Likelihood EstimatorIntroduction to Maximum Likelihood Estimator
Introduction to Maximum Likelihood Estimator
Amir Al-Ansary
 
Lasso and ridge regression
Lasso and ridge regressionLasso and ridge regression
Lasso and ridge regression
SreerajVA
 
Data Science - Part XII - Ridge Regression, LASSO, and Elastic Nets
Data Science - Part XII - Ridge Regression, LASSO, and Elastic NetsData Science - Part XII - Ridge Regression, LASSO, and Elastic Nets
Data Science - Part XII - Ridge Regression, LASSO, and Elastic Nets
Derek Kane
 
Bayesian inference
Bayesian inferenceBayesian inference
Bayesian inference
CharthaGaglani
 
Naive Bayes Presentation
Naive Bayes PresentationNaive Bayes Presentation
Naive Bayes Presentation
Md. Enamul Haque Chowdhury
 
Probability Distributions
Probability DistributionsProbability Distributions
Probability Distributions
CIToolkit
 
binomial distribution
binomial distributionbinomial distribution
binomial distribution
Mmedsc Hahm
 
Bayesian learning
Bayesian learningBayesian learning
Bayesian learning
Rogier Geertzema
 
Basic probability concept
Basic probability conceptBasic probability concept
Basic probability concept
Mmedsc Hahm
 
Inductive bias
Inductive biasInductive bias
Inductive bias
swapnac12
 
Probability distribution
Probability distributionProbability distribution
Probability distributionRanjan Kumar
 
probability
probabilityprobability
probability
Dr.Muhammad Omer
 
Unit 1-probability
Unit 1-probabilityUnit 1-probability
Unit 1-probability
Rai University
 
Introduction to Probability and Bayes' Theorom
Introduction to Probability and Bayes' TheoromIntroduction to Probability and Bayes' Theorom
Introduction to Probability and Bayes' Theorom
Yugal Gupta
 
Addition rule and multiplication rule
Addition rule and multiplication rule  Addition rule and multiplication rule
Addition rule and multiplication rule
Long Beach City College
 
Fundamentals Probability 08072009
Fundamentals Probability 08072009Fundamentals Probability 08072009
Fundamentals Probability 08072009
Sri Harsha gadiraju
 
Continuous Random Variables
Continuous Random VariablesContinuous Random Variables
Continuous Random Variables
mathscontent
 

What's hot (20)

Bayesian networks
Bayesian networksBayesian networks
Bayesian networks
 
Ridge regression
Ridge regressionRidge regression
Ridge regression
 
Introduction to Maximum Likelihood Estimator
Introduction to Maximum Likelihood EstimatorIntroduction to Maximum Likelihood Estimator
Introduction to Maximum Likelihood Estimator
 
Lasso and ridge regression
Lasso and ridge regressionLasso and ridge regression
Lasso and ridge regression
 
Data Science - Part XII - Ridge Regression, LASSO, and Elastic Nets
Data Science - Part XII - Ridge Regression, LASSO, and Elastic NetsData Science - Part XII - Ridge Regression, LASSO, and Elastic Nets
Data Science - Part XII - Ridge Regression, LASSO, and Elastic Nets
 
Bayesian inference
Bayesian inferenceBayesian inference
Bayesian inference
 
Naive Bayes Presentation
Naive Bayes PresentationNaive Bayes Presentation
Naive Bayes Presentation
 
Probability Distributions
Probability DistributionsProbability Distributions
Probability Distributions
 
binomial distribution
binomial distributionbinomial distribution
binomial distribution
 
Bayesian learning
Bayesian learningBayesian learning
Bayesian learning
 
Basic probability concept
Basic probability conceptBasic probability concept
Basic probability concept
 
Inductive bias
Inductive biasInductive bias
Inductive bias
 
Probability distribution
Probability distributionProbability distribution
Probability distribution
 
probability
probabilityprobability
probability
 
Unit 1-probability
Unit 1-probabilityUnit 1-probability
Unit 1-probability
 
Introduction to Probability and Bayes' Theorom
Introduction to Probability and Bayes' TheoromIntroduction to Probability and Bayes' Theorom
Introduction to Probability and Bayes' Theorom
 
Hasse diagram
Hasse diagramHasse diagram
Hasse diagram
 
Addition rule and multiplication rule
Addition rule and multiplication rule  Addition rule and multiplication rule
Addition rule and multiplication rule
 
Fundamentals Probability 08072009
Fundamentals Probability 08072009Fundamentals Probability 08072009
Fundamentals Probability 08072009
 
Continuous Random Variables
Continuous Random VariablesContinuous Random Variables
Continuous Random Variables
 

Similar to Bays theorem of probability

Bayes (1).pptx
Bayes (1).pptxBayes (1).pptx
Bayes (1).pptx
HarshitSingh334328
 
Many decisions are based on beliefs concerning the likelihoo.docx
Many decisions are based on beliefs concerning the likelihoo.docxMany decisions are based on beliefs concerning the likelihoo.docx
Many decisions are based on beliefs concerning the likelihoo.docx
alfredacavx97
 
Applied bayesian statistics
Applied bayesian statisticsApplied bayesian statistics
Applied bayesian statisticsSpringer
 
Naive bayes
Naive bayesNaive bayes
On Severity, the Weight of Evidence, and the Relationship Between the Two
On Severity, the Weight of Evidence, and the Relationship Between the TwoOn Severity, the Weight of Evidence, and the Relationship Between the Two
On Severity, the Weight of Evidence, and the Relationship Between the Two
jemille6
 
Conditional-probability-and-Bioinformatics.pptx
Conditional-probability-and-Bioinformatics.pptxConditional-probability-and-Bioinformatics.pptx
Conditional-probability-and-Bioinformatics.pptx
RITHIKA R S
 

Similar to Bays theorem of probability (9)

Bayes (1).pptx
Bayes (1).pptxBayes (1).pptx
Bayes (1).pptx
 
Many decisions are based on beliefs concerning the likelihoo.docx
Many decisions are based on beliefs concerning the likelihoo.docxMany decisions are based on beliefs concerning the likelihoo.docx
Many decisions are based on beliefs concerning the likelihoo.docx
 
Bayesian ijupls
Bayesian ijuplsBayesian ijupls
Bayesian ijupls
 
Applied bayesian statistics
Applied bayesian statisticsApplied bayesian statistics
Applied bayesian statistics
 
Naive bayes
Naive bayesNaive bayes
Naive bayes
 
Bsp Spresentation
Bsp SpresentationBsp Spresentation
Bsp Spresentation
 
On Severity, the Weight of Evidence, and the Relationship Between the Two
On Severity, the Weight of Evidence, and the Relationship Between the TwoOn Severity, the Weight of Evidence, and the Relationship Between the Two
On Severity, the Weight of Evidence, and the Relationship Between the Two
 
Capits Presentation
Capits PresentationCapits Presentation
Capits Presentation
 
Conditional-probability-and-Bioinformatics.pptx
Conditional-probability-and-Bioinformatics.pptxConditional-probability-and-Bioinformatics.pptx
Conditional-probability-and-Bioinformatics.pptx
 

More from mayank mulchandani

A Comparative Study on Home Loan of ICICI & SBI Bank
A Comparative Study on Home Loan of ICICI  & SBI BankA Comparative Study on Home Loan of ICICI  & SBI Bank
A Comparative Study on Home Loan of ICICI & SBI Bank
mayank mulchandani
 
Difference between collective bargaining & negotiation
Difference between collective bargaining & negotiationDifference between collective bargaining & negotiation
Difference between collective bargaining & negotiation
mayank mulchandani
 
Summer Internship Report 2019 ppt
Summer Internship Report 2019 pptSummer Internship Report 2019 ppt
Summer Internship Report 2019 ppt
mayank mulchandani
 
Summer Internship Report 2019
Summer Internship Report 2019Summer Internship Report 2019
Summer Internship Report 2019
mayank mulchandani
 
Comparative Analysis On Mutual Fund Scheme
Comparative Analysis On Mutual Fund SchemeComparative Analysis On Mutual Fund Scheme
Comparative Analysis On Mutual Fund Scheme
mayank mulchandani
 
Choice Based Credit System CBCS
Choice Based Credit System CBCSChoice Based Credit System CBCS
Choice Based Credit System CBCS
mayank mulchandani
 
Choice Based Credit System CBCS
 Choice Based Credit System CBCS Choice Based Credit System CBCS
Choice Based Credit System CBCS
mayank mulchandani
 
Concept of motive & motivation & mallows need or hierarchy theory
Concept of motive & motivation & mallows need or hierarchy theoryConcept of motive & motivation & mallows need or hierarchy theory
Concept of motive & motivation & mallows need or hierarchy theory
mayank mulchandani
 
Cost Control & Cost Reduction ppt
Cost Control & Cost Reduction pptCost Control & Cost Reduction ppt
Cost Control & Cost Reduction ppt
mayank mulchandani
 
MNCs Features, significance, Role , Impact of MNCs on Indian economy.
 MNCs Features, significance, Role , Impact of MNCs on Indian economy. MNCs Features, significance, Role , Impact of MNCs on Indian economy.
MNCs Features, significance, Role , Impact of MNCs on Indian economy.
mayank mulchandani
 
Input,output & storage device ppt
Input,output & storage device pptInput,output & storage device ppt
Input,output & storage device ppt
mayank mulchandani
 

More from mayank mulchandani (11)

A Comparative Study on Home Loan of ICICI & SBI Bank
A Comparative Study on Home Loan of ICICI  & SBI BankA Comparative Study on Home Loan of ICICI  & SBI Bank
A Comparative Study on Home Loan of ICICI & SBI Bank
 
Difference between collective bargaining & negotiation
Difference between collective bargaining & negotiationDifference between collective bargaining & negotiation
Difference between collective bargaining & negotiation
 
Summer Internship Report 2019 ppt
Summer Internship Report 2019 pptSummer Internship Report 2019 ppt
Summer Internship Report 2019 ppt
 
Summer Internship Report 2019
Summer Internship Report 2019Summer Internship Report 2019
Summer Internship Report 2019
 
Comparative Analysis On Mutual Fund Scheme
Comparative Analysis On Mutual Fund SchemeComparative Analysis On Mutual Fund Scheme
Comparative Analysis On Mutual Fund Scheme
 
Choice Based Credit System CBCS
Choice Based Credit System CBCSChoice Based Credit System CBCS
Choice Based Credit System CBCS
 
Choice Based Credit System CBCS
 Choice Based Credit System CBCS Choice Based Credit System CBCS
Choice Based Credit System CBCS
 
Concept of motive & motivation & mallows need or hierarchy theory
Concept of motive & motivation & mallows need or hierarchy theoryConcept of motive & motivation & mallows need or hierarchy theory
Concept of motive & motivation & mallows need or hierarchy theory
 
Cost Control & Cost Reduction ppt
Cost Control & Cost Reduction pptCost Control & Cost Reduction ppt
Cost Control & Cost Reduction ppt
 
MNCs Features, significance, Role , Impact of MNCs on Indian economy.
 MNCs Features, significance, Role , Impact of MNCs on Indian economy. MNCs Features, significance, Role , Impact of MNCs on Indian economy.
MNCs Features, significance, Role , Impact of MNCs on Indian economy.
 
Input,output & storage device ppt
Input,output & storage device pptInput,output & storage device ppt
Input,output & storage device ppt
 

Recently uploaded

CLASS 11 CBSE B.St Project AIDS TO TRADE - INSURANCE
CLASS 11 CBSE B.St Project AIDS TO TRADE - INSURANCECLASS 11 CBSE B.St Project AIDS TO TRADE - INSURANCE
CLASS 11 CBSE B.St Project AIDS TO TRADE - INSURANCE
BhavyaRajput3
 
Welcome to TechSoup New Member Orientation and Q&A (May 2024).pdf
Welcome to TechSoup   New Member Orientation and Q&A (May 2024).pdfWelcome to TechSoup   New Member Orientation and Q&A (May 2024).pdf
Welcome to TechSoup New Member Orientation and Q&A (May 2024).pdf
TechSoup
 
The French Revolution Class 9 Study Material pdf free download
The French Revolution Class 9 Study Material pdf free downloadThe French Revolution Class 9 Study Material pdf free download
The French Revolution Class 9 Study Material pdf free download
Vivekanand Anglo Vedic Academy
 
Sha'Carri Richardson Presentation 202345
Sha'Carri Richardson Presentation 202345Sha'Carri Richardson Presentation 202345
Sha'Carri Richardson Presentation 202345
beazzy04
 
Sectors of the Indian Economy - Class 10 Study Notes pdf
Sectors of the Indian Economy - Class 10 Study Notes pdfSectors of the Indian Economy - Class 10 Study Notes pdf
Sectors of the Indian Economy - Class 10 Study Notes pdf
Vivekanand Anglo Vedic Academy
 
How to Create Map Views in the Odoo 17 ERP
How to Create Map Views in the Odoo 17 ERPHow to Create Map Views in the Odoo 17 ERP
How to Create Map Views in the Odoo 17 ERP
Celine George
 
Template Jadual Bertugas Kelas (Boleh Edit)
Template Jadual Bertugas Kelas (Boleh Edit)Template Jadual Bertugas Kelas (Boleh Edit)
Template Jadual Bertugas Kelas (Boleh Edit)
rosedainty
 
Instructions for Submissions thorugh G- Classroom.pptx
Instructions for Submissions thorugh G- Classroom.pptxInstructions for Submissions thorugh G- Classroom.pptx
Instructions for Submissions thorugh G- Classroom.pptx
Jheel Barad
 
How to Make a Field invisible in Odoo 17
How to Make a Field invisible in Odoo 17How to Make a Field invisible in Odoo 17
How to Make a Field invisible in Odoo 17
Celine George
 
The Roman Empire A Historical Colossus.pdf
The Roman Empire A Historical Colossus.pdfThe Roman Empire A Historical Colossus.pdf
The Roman Empire A Historical Colossus.pdf
kaushalkr1407
 
special B.ed 2nd year old paper_20240531.pdf
special B.ed 2nd year old paper_20240531.pdfspecial B.ed 2nd year old paper_20240531.pdf
special B.ed 2nd year old paper_20240531.pdf
Special education needs
 
Palestine last event orientationfvgnh .pptx
Palestine last event orientationfvgnh .pptxPalestine last event orientationfvgnh .pptx
Palestine last event orientationfvgnh .pptx
RaedMohamed3
 
Ethnobotany and Ethnopharmacology ......
Ethnobotany and Ethnopharmacology ......Ethnobotany and Ethnopharmacology ......
Ethnobotany and Ethnopharmacology ......
Ashokrao Mane college of Pharmacy Peth-Vadgaon
 
How libraries can support authors with open access requirements for UKRI fund...
How libraries can support authors with open access requirements for UKRI fund...How libraries can support authors with open access requirements for UKRI fund...
How libraries can support authors with open access requirements for UKRI fund...
Jisc
 
Additional Benefits for Employee Website.pdf
Additional Benefits for Employee Website.pdfAdditional Benefits for Employee Website.pdf
Additional Benefits for Employee Website.pdf
joachimlavalley1
 
The Challenger.pdf DNHS Official Publication
The Challenger.pdf DNHS Official PublicationThe Challenger.pdf DNHS Official Publication
The Challenger.pdf DNHS Official Publication
Delapenabediema
 
MARUTI SUZUKI- A Successful Joint Venture in India.pptx
MARUTI SUZUKI- A Successful Joint Venture in India.pptxMARUTI SUZUKI- A Successful Joint Venture in India.pptx
MARUTI SUZUKI- A Successful Joint Venture in India.pptx
bennyroshan06
 
The approach at University of Liverpool.pptx
The approach at University of Liverpool.pptxThe approach at University of Liverpool.pptx
The approach at University of Liverpool.pptx
Jisc
 
Operation Blue Star - Saka Neela Tara
Operation Blue Star   -  Saka Neela TaraOperation Blue Star   -  Saka Neela Tara
Operation Blue Star - Saka Neela Tara
Balvir Singh
 
Polish students' mobility in the Czech Republic
Polish students' mobility in the Czech RepublicPolish students' mobility in the Czech Republic
Polish students' mobility in the Czech Republic
Anna Sz.
 

Recently uploaded (20)

CLASS 11 CBSE B.St Project AIDS TO TRADE - INSURANCE
CLASS 11 CBSE B.St Project AIDS TO TRADE - INSURANCECLASS 11 CBSE B.St Project AIDS TO TRADE - INSURANCE
CLASS 11 CBSE B.St Project AIDS TO TRADE - INSURANCE
 
Welcome to TechSoup New Member Orientation and Q&A (May 2024).pdf
Welcome to TechSoup   New Member Orientation and Q&A (May 2024).pdfWelcome to TechSoup   New Member Orientation and Q&A (May 2024).pdf
Welcome to TechSoup New Member Orientation and Q&A (May 2024).pdf
 
The French Revolution Class 9 Study Material pdf free download
The French Revolution Class 9 Study Material pdf free downloadThe French Revolution Class 9 Study Material pdf free download
The French Revolution Class 9 Study Material pdf free download
 
Sha'Carri Richardson Presentation 202345
Sha'Carri Richardson Presentation 202345Sha'Carri Richardson Presentation 202345
Sha'Carri Richardson Presentation 202345
 
Sectors of the Indian Economy - Class 10 Study Notes pdf
Sectors of the Indian Economy - Class 10 Study Notes pdfSectors of the Indian Economy - Class 10 Study Notes pdf
Sectors of the Indian Economy - Class 10 Study Notes pdf
 
How to Create Map Views in the Odoo 17 ERP
How to Create Map Views in the Odoo 17 ERPHow to Create Map Views in the Odoo 17 ERP
How to Create Map Views in the Odoo 17 ERP
 
Template Jadual Bertugas Kelas (Boleh Edit)
Template Jadual Bertugas Kelas (Boleh Edit)Template Jadual Bertugas Kelas (Boleh Edit)
Template Jadual Bertugas Kelas (Boleh Edit)
 
Instructions for Submissions thorugh G- Classroom.pptx
Instructions for Submissions thorugh G- Classroom.pptxInstructions for Submissions thorugh G- Classroom.pptx
Instructions for Submissions thorugh G- Classroom.pptx
 
How to Make a Field invisible in Odoo 17
How to Make a Field invisible in Odoo 17How to Make a Field invisible in Odoo 17
How to Make a Field invisible in Odoo 17
 
The Roman Empire A Historical Colossus.pdf
The Roman Empire A Historical Colossus.pdfThe Roman Empire A Historical Colossus.pdf
The Roman Empire A Historical Colossus.pdf
 
special B.ed 2nd year old paper_20240531.pdf
special B.ed 2nd year old paper_20240531.pdfspecial B.ed 2nd year old paper_20240531.pdf
special B.ed 2nd year old paper_20240531.pdf
 
Palestine last event orientationfvgnh .pptx
Palestine last event orientationfvgnh .pptxPalestine last event orientationfvgnh .pptx
Palestine last event orientationfvgnh .pptx
 
Ethnobotany and Ethnopharmacology ......
Ethnobotany and Ethnopharmacology ......Ethnobotany and Ethnopharmacology ......
Ethnobotany and Ethnopharmacology ......
 
How libraries can support authors with open access requirements for UKRI fund...
How libraries can support authors with open access requirements for UKRI fund...How libraries can support authors with open access requirements for UKRI fund...
How libraries can support authors with open access requirements for UKRI fund...
 
Additional Benefits for Employee Website.pdf
Additional Benefits for Employee Website.pdfAdditional Benefits for Employee Website.pdf
Additional Benefits for Employee Website.pdf
 
The Challenger.pdf DNHS Official Publication
The Challenger.pdf DNHS Official PublicationThe Challenger.pdf DNHS Official Publication
The Challenger.pdf DNHS Official Publication
 
MARUTI SUZUKI- A Successful Joint Venture in India.pptx
MARUTI SUZUKI- A Successful Joint Venture in India.pptxMARUTI SUZUKI- A Successful Joint Venture in India.pptx
MARUTI SUZUKI- A Successful Joint Venture in India.pptx
 
The approach at University of Liverpool.pptx
The approach at University of Liverpool.pptxThe approach at University of Liverpool.pptx
The approach at University of Liverpool.pptx
 
Operation Blue Star - Saka Neela Tara
Operation Blue Star   -  Saka Neela TaraOperation Blue Star   -  Saka Neela Tara
Operation Blue Star - Saka Neela Tara
 
Polish students' mobility in the Czech Republic
Polish students' mobility in the Czech RepublicPolish students' mobility in the Czech Republic
Polish students' mobility in the Czech Republic
 

Bays theorem of probability

  • 1. THROUGH ONE EXAMPLE…………. MADE BY:- MAYANK MULCHANDANI
  • 2.  In probability theory and statistics, Bayes' theorem (alternatively Bayes' law or Bayes' rule) describes the probability of an event, based on prior knowledge of conditions that might be related to the event. For example, if cancer is related to age, then, using Bayes' theorem, a person's age can be used to more accurately assess the probability that they have cancer, compared to the assessment of the probability of cancer made without knowledge of the person's age.
  • 3. One of the many applications of Bayes' theorem is Bayesian inference, a particular approach to statistical inference. When applied, the probabilities involved in Bayes' theorem may have different probability interpretations. With the Bayesian probability interpretation the theorem expresses how a subjective degree of belief should rationally change to account for availability of related evidence. Bayesian inference is fundamental to Bayesian statistics.
  • 4.  We have two bags contains Red & black Balls.. A B RED 2 BLACK 3 A RED 3 BLACK 4
  • 5. Case 1: what is the probability of get’s Red Ball from bag A??? { bag A is already selected} Should be written as… P(R/A) = 2/5
  • 6. Case 2: what is the probability of Red Ball drawn from bag A??? P(A ∩ R) = P(A)P(R/A) Probability of Red ball and from bag A
  • 7.  Case 3: what is the probability of Red Ball??? P(R)=P(A ∩ R) + P(B ∩ R) Probability of getting red ball from bag A Probability of getting red ball from bag B
  • 8.  Case 4: Given that red ball is drawn .what is the probability that the Ball is from bag A ???  P(A/R)= P(A ∩ R) P(A ∩ R) + P(B ∩ R)
  • 9.  Putting P(A ∩ R) = P(A)P(R/A) P(R)=P(A ∩ R) + P(B ∩ R) So… P(A/R)= P(A)P(R/A) P(A ∩ R) + P(B ∩ R) Bays theorem