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DataSciencePres
DataSciencePres
 
 
Jags
Jags
 
 
BEST.hs
BEST.hs
 
 
Bayes.bib
Bayes.bib
 
 
BernMetropolis.lhs
BernMetropolis.lhs
 
 
BernMetropolisTemplate.R
BernMetropolisTemplate.R
 
 
BernMetropolisTemplate.hs
BernMetropolisTemplate.hs
 
 
ConjMCMCSimple.lhs
ConjMCMCSimple.lhs
 
 
FuncBernMetropolis.R
FuncBernMetropolis.R
 
 
LinReg.lhs
LinReg.lhs
 
 
LinRegAux.hs
LinRegAux.hs
 
 
LinRegSingle.hs
LinRegSingle.hs
 
 
PopGrowthMCMC.lhs
PopGrowthMCMC.lhs
 
 
PopGrowthMCMCChart.hs
PopGrowthMCMCChart.hs
 
 
PopGrowthMCMCMain.hs
PopGrowthMCMCMain.hs
 
 
README
README
 
 
StudentAux.hs
StudentAux.hs
 
 
StudentTest.lhs
StudentTest.lhs
 
 
TestFu.hs
TestFu.hs
 
 

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This was going to be an EDSL in Haskell to Jags (after all there is no
need to re-invent the Gibbs sampler wheel) but the world has moved
on. First let us do some examples of Bayesian regression using
Hamiltonian Monte Carlo and then maybe we can think about interfacing
to STAN.

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Bayesian Analysis in Haskell

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