Mhstep = function(bold,lupold,v,w,t,x) {
# takes a Metropolis-Hastings step for the log-linear Poisson model
#   with one covariate, a Gaussian prior, and a Gaussian random walk
#   proposal
# INPUTS:
#   bold = 2-vector of old value
#   lupold = scalar of log unnormed post at bold
#   v = 2-vector of prior variances
#   w = 2-vector of variances for metropolis-hastings steps
#   t = suff stat
#   x = n-vector of predictors
# OUTPUTS:
#   bnew = 2-vector of new values
#   lupnew = scalar of log unnormed post at bnew
bnew = bold + rnorm(2,0,sqrt(w))
lupnew = Lup(bnew,v,t,x)
dlup = lupnew - lupold
if(dlup<0) {
   alpha = exp(dlup)
   if(runif(1) > alpha) {
      bnew = bold
      lupnew = lupold
   }
}
mhout = list(bnew=bnew,lupnew=lupnew)
return(mhout)
}