My version of the scaled wishart was slower, but Abe had a few
suggestions to speed it up.
For the simple varying intercepts/slopes, the pymc examples should be
faster than R/Jags. I don't recall exactly from memory, but I think I
got about a 4x speedup.
-Whit
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Yup - the second example is slow because it is looped, which is the pymc
downside. On the upside, Python likes arrays, and this creates the
speedier solution in the first example.
As for why it doesn't run as fast as R - and forgive this question - but
are you comparing pymc to an R-coded mcmc scheme?
Aaron
I see:
warmstrong@krypton:/tmp/stats.tmp$ grep 'n.iter' *.R
16.3-BUGS.R: "radon.1.bug", n.chains=3, n.iter=10, debug=TRUE)
16.3-JAGS.R: "radon.1.bug", n.chains=3, n.iter=10)
warmstrong@krypton:/tmp/stats.tmp$
vs the pymc example 10k iterations, 1 chain:
warmstrong@krypton:/tmp/stats.tmp$ grep 'iter' *.py
16.3.py:M.sample(iter=50e3, burn=10e3, thin=5)
warmstrong@krypton:/tmp/stats.tmp$
-Whit