ispoi {InvStablePrior}

Bayesian inference for the true mean of Poisson distribution.

Description

Generates random numbers from the prior and posterior distributions of the inverse stable-Poisson model. The random variates can be used to simulate prior and posterior predictive distributions as well.

Usage

ispoi(x, B, alp, rho)

Arguments

x

vector of Poisson count data.

B

test size for the adaptive rejection sampling algorithm.

alp

value between 0 and 1 that controls the shape of the inverse stable prior.

rho

positive value that scales the mean of the inverse stable prior.

Value

list consisting of the vectors of random numbers from the prior and posterior distributions, the accepted sample size, and the acceptance probability of the adaptive rejection sampling procedure (Algorithm 2 of the first reference below).

References

Cahoy and Sedransk (2019). Inverse stable prior for exponential models. Journal of Statistical Theory and Practice, 13, Article 29. <doi:10.1007/s42519-018-0027-2>

Meerschaert and Straka (2013). Inverse stable subordinators. Math. Model. Nat. Phenom., 8(2), 1-16. <doi:10.1051/mmnp/20138201>

Mainardi, Mura, and Pagnini (2010). The M-Wright Function in Time-Fractional Diffusion Processes: A Tutorial Survey. Int. J. Differ. Equ., Volume 2010. <doi:10.1155/2010/104505>

Examples

alp=0.9
dat=rpois(50,lambda=10)
rho=mean(dat)
#rho=mean(dat) + 3*sd(dat)
#rho=mean(dat) - 3*sd(dat)

#a=length(dat)
#b=sum(dat)
#rho=optimize(function(r){exp(-b)*(b/a)^b - (r^b)*exp(-a*r)}, c(0,20),  tol=10^(-50)  )$min

out= ispoi(dat, B=1000000, alp , rho)
#prior samples
thetprior=unlist(out[2])
summary(thetprior)

#posterior samples
thet=unlist(out[1])

#95% Credible intervals
quantile (thet, c(0.025,0.975) )
summary(thet)

#The accepted sample size:
unlist(out[3])

#The acceptance probability:
unlist(out[4])

#Plotting with normalization to have a maximum of 1
#for comparing prior and posterior
out2=density(thet)
ymaxpost=max(out2$y)
out3=density(thetprior)
ymaxprior=max(out3$y)
plot(out2$x,out2$y/ymaxpost, xlim=c(0,15), col="blue", type="l",
 xlab="theta", ylab="density", lwd=2, frame.plot=FALSE)
lines(out3$x,out3$y/ymaxprior,lwd=2,col="red")
#points(dat,rep(0,length(dat)), pch='*')


#Generating 1000 random numbers from the Inverse Stable (alpha=0.4,rho=5) prior
U1 = runif(1000)
U2 = runif(1000)
alp=0.4
rho=5
stab = ( ( sin(alp*pi*U1)*sin((1-alp)*pi*U1)^(1/alp-1) )
/ (  ( sin(pi*U1)^(1/alp) )*abs(log(U2))^(1/alp-1))  )
#Inverse stable random numbers are below:
#rho*stab^(-alp)