**Freakonometrics - Tag - R-english**, and kindly contributed to R-bloggers)

Last week, during the workshop on *Statistical Methods for
Meteorology and
Climate Change* (here),
I discovered how powerful bayesian techniques could be, and that there
were more and more bayesian statisticians. So, if I was to fully
understand
applied statisticians in conferences and workshops, I really have to
understand basics of bayesian statistics. I have published some time
ago some posts on bayesian statistics applied to actuarial problems (here or there), but so far, I always thought that *bayesian* was a synonym for *magician*.

To be honest, I am a Muggle, and I have not been trained as a bayesian. But I can be an opportunist...

So I decided to publish some posts on bayesian techniques, in order to
prove that it is actually not that difficult to implement.

As far as I understand it, in bayesian statistics, the *parameter* is considered as a random variable (which is also the case, in *classical* mathematical statistics). But here, here assume that this parameter does have a
parametric distribution....

Consider a classical statistical problem: assume we have a sample i.i.d. with distribution . Here we note

So far it was simple. The idea is then to consider the posterior distribution of , given the observations . Thus, we need to compute the distribution of which is here extremely simple (due to properties of the Gaussian distribution), i.e.

where

And them, it becomes extremely natural to consider as an estimator of given our sample data (and thus, we also have a confidence interval since we know the distribution of given the observations ).

In order to be sure that we understood, consider now a heads and tails problem, i.e. . Note, first, that theta has support . So we need a distribution on that support. Why not a beta distribution ? E.g.

Thus,

and

From Bayes formula,

and we get easily

which is the density of a Beta distribution, i.e.

The estimator proposed is then the expected value of that conditional distribution,

Note that

Further, it is possible to derive confidence intervals using quantiles of the posterior distribution.

On the graphs below, we consider the following heads/tails sample

A first idea is to consider a uniform prior distribution.

A second idea is to consider an asymmetric beta distribution. First, with an asymmetry on the left,

If we compare the four models, we obtain (the plain black line is the Gaussian approximated distribution for the empirical mean), and red lines are obtained from

*prior*beta distributions

a1=1; b1=1

D1[1,]=dbeta(u,a,b)

a2=4; b2=2

D2[1,]=dbeta(u,a,b)

a3=2; b3=4

D3[1,]=dbeta(u,a,b)

setseed(1)

S=sample(0:1,size=100,replace=TRUE)

COULEUR=rev(rainbow(120))

D1=D2=D3=D4=matrix(NA,101,length(u))

for(s in 1:100){

y=sum(S[1:s])

D1[s+1,]=dbeta(u,a1+y,s-y+b1)

D2[s+1,]=dbeta(u,a2+y,s-y+b2)

D3[s+1,]=dbeta(u,a3+y,s-y+b3)

D4[s+1,]=dnorm(u,y/s,sqrt(y/s*(1-y/s)/s))

plot(u,D1[1,],col="black",type="l",ylim=c(0,8),

xlab="",ylab="")

for(i in 1:s){lines(u,D1[1+i,],col=COULEUR[i])}

points(y/s,0,pch=3,cex=2)

plot(u,D2[1,],col="black",type="l",ylim=c(0,8),

xlab="",ylab="")

for(i in 1:s){lines(u,D2[1+i,],col=COULEUR[i])}

points(y/s,0,pch=3,cex=2)

plot(u,D3[1,],col="black",type="l",ylim=c(0,8),

xlab="",ylab="")

for(i in 1:s){lines(u,D3[1+i,],col=COULEUR[i])}

points(y/s,0,pch=3,cex=2)

plot(u,D4[1,],col="white",type="l",ylim=c(0,8),

xlab="",ylab="")

for(i in 1:s){lines(u,D4[1+i,],col=COULEUR[i])}

points(y/s,0,pch=3,cex=2)

plot(u,D4[s+1,],col="black",lwd=2,type="l",

ylim=c(0,8),xlab="",ylab="")

lines(u,D1[1+i,],col="blue")

lines(u,D2[1+i,],col="red")

lines(u,D3[1+i,],col="purple")

points(y/s,0,pch=3,cex=2)

}

So far, I have two questions that naturally show up

- is it possible to start with a
*neutral*prior distribution, non informative ? - what if we are no longer working with conjugate distributions ?

*to be continued*...

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**Freakonometrics - Tag - R-english**.

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