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Substitute unnecessary matrix inversion, use linear solve instead. #74

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Aug 11, 2020
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Substitute unnecessary matrix inversion, use linear solve instead. #74

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9 changes: 2 additions & 7 deletions src/MCMC.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -33,8 +33,6 @@ struct MCMCObj{FT<:AbstractFloat, IT<:Int}
obs_sample::Vector{FT}
"covariance of the observational noise"
obs_noise_cov::Array{FT, 2}
"inverse of obs_noise_cov"
obs_noise_covinv::Array{FT, 2}
"array of length N_parameters with the parameters' prior distributions"
prior::Array{Prior, 1}
"MCMC step size"
Expand Down Expand Up @@ -96,14 +94,12 @@ function MCMCObj(
param = param_init_copy
log_posterior = [nothing]
iter = [1]
obs_noise_covinv = inv(obs_noise_cov)
accept = [0]
if algtype != "rwm"
error("only random walk metropolis 'rwm' is implemented so far")
end
MCMCObj{FT,IT}(obs_sample,
obs_noise_cov,
obs_noise_covinv,
priors,
[step],
burnin,
Expand Down Expand Up @@ -167,12 +163,11 @@ function log_likelihood(mcmc::MCMCObj{FT},
log_rho = FT[0]
if gvar == nothing
diff = g - mcmc.obs_sample
log_rho[1] = -FT(0.5) * diff' * mcmc.obs_noise_covinv * diff
log_rho[1] = -FT(0.5) * diff' * (mcmc.obs_noise_cov \ diff)
else
gcov_inv = inv(Diagonal(gvar))
log_gpfidelity = -FT(0.5) * log(det(Diagonal(gvar))) # = -0.5 * sum(log.(gvar))
diff = g - mcmc.obs_sample
log_rho[1] = -FT(0.5) * diff' * gcov_inv * diff + log_gpfidelity
log_rho[1] = -FT(0.5) * diff' * (Diagonal(gvar) \ diff) + log_gpfidelity
end
return log_rho[1]
end
Expand Down
1 change: 0 additions & 1 deletion test/MCMC/runtests.jl
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Expand Up @@ -69,7 +69,6 @@ using CalibrateEmulateSample.GPEmulator
# 1.0/sqrt(0.05) * obs_sample ≈ 4.472
@test mcmc.obs_sample ≈ [4.472] atol=1e-2
@test mcmc.obs_noise_cov == σ2_y
@test mcmc.obs_noise_covinv == inv(σ2_y)
@test mcmc.burnin == burnin
@test mcmc.algtype == mcmc_alg
@test mcmc.iter[1] == max_iter + 1
Expand Down