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**S**imulating a bivariate normal under the constraint (or conditional to the fact) that x²-y²=1 (a non-linear zero measure curve in the 2-dimensional Euclidean space) is not that easy: if running a random walk along that curve (by running a random walk on y and deducing x as x²=y²+1 and accepting with a Metropolis-Hastings ratio based on the bivariate normal density), the outcome differs from the target predicted by a change of variable and the proper derivation of the conditional. The *above* graph resulting from the R code *below* illustrates the discrepancy!

targ=function(y){ exp(-y^2)/(1.52*sqrt(1+y^2))} T=10^5 Eps=3 ys=xs=rep(runif(1),T) xs[1]=sqrt(1+ys[1]^2) for (t in 2:T){ propy=runif(1,-Eps,Eps)+ys[t-1] propx=sqrt(1+propy^2) ace=(runif(1)<(dnorm(propy)*dnorm(propx))/ (dnorm(ys[t-1])*dnorm(xs[t-1]))) if (ace){ ys[t]=propy;xs[t]=propx }else{ ys[t]=ys[t-1];xs[t]=xs[t-1]}}

If instead we add the proper Jacobian as in

ace=(runif(1)<(dnorm(propy)*dnorm(propx)/propx)/ (dnorm(ys[t-1])*dnorm(xs[t-1])/xs[t-1]))

the fit is there. My open question is how to make this derivation generic, i.e. without requiring the (dreaded) computation of the (dreadful) Jacobian.

Filed under: R, Statistics Tagged: conditional density, Hastings-Metropolis sampler, Jacobian, MCMC, measure theory, measure zero set, projected measure, random walk

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