Bayesian Statistics: Inwards A Layman Terms

 What is Bayesian Statistics: (I volition endeavour to explicate inwards slow terms)

 

Often researchers investigating an unknown population parameter receive got information available from other sources inwards advance of the report that provides a rigid indication of what values the parameter is probable to take. This additional information powerfulness hold out inwards a cast that cannot hold out incorporated direct inwards the electrical current study. The classical statistical approach offers no reach for the researchers to receive got this additional information into account. However, the Bayesian statistics is the approach which allows to receive got this additional information into describe of piece of occupation concern human relationship piece estimating a population parameter.

 

Let me explicate yous alongside the help of an example:

 

4 entitle races had been done betwixt Mr. A too Mr. B. 

Out of which A has won three races too B has won 1 race. So, on whom are yous going to bet your coin inwards the adjacent race?





You volition Say Mr. A because P(A) = 0.75 too P(B) = 0.25

So your initial guess virtually B is P(B) = 0.25

Now I volition hand yous additional information say, at that spot was a pelting when Mr. B won too at that spot was pelting in 1 trial when Mr. A won. And inwards the adjacent check at that spot volition definitely hold out a rain.


So immediately I enquire yous in 1 trial again on whom volition yous bet your money?

Let’s decode the answer:

1.  P(R) = 0.50 (Because pelting happened twice out of four matches)

2. P(R|B) = 1 (Because whenever Mr. B won at that spot was a rain)

So I desire to discovery out that what is probability that inwards the adjacent race Mr. B volition won if it is given that at that spot volition hold out a rain:

P(B|R) = P(R|B)*P(B)/P(R) = 0.50


I promise yous know how this formula comes upwards otherwise yous tin advert me inwards comments I volition tell yous how.


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Conclusion: Initially nosotros comes upwards alongside an reply that P(B) = 0.25 which is my prior guess too hence I hand additional information virtually pelting which nosotros incorporated inwards the cast of conditional probability i.e. P(R|B) = 1 too hence ultimately nosotros discovery P(B|R) which is my posterior probability.


So yous come across how alongside the help of Bayesian statistics I incorporated additional information into my electrical current report too how my value changes from 0.25 to 0.50.

 

Statistics seems slow now. 😊

Its an fine art too yous are an artist.


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