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@@ -1,6 +1,25 @@
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import numpy as np
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import scipy
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+
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+def setup_scipy_distr(distr_str, n_pars, parse_pars):
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+
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+
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+ # log of pdf for all choice
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+ def log_pdfs(x, pars, choice = 2):
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+ logpdf = eval(distr_str).logpdf
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+ if choice in [0,1]: return logpdf(x, **parse_pars(pars))
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+ return (logpdf(x, **parse_pars(pars)), logpdf(x, **parse_pars(pars[n_pars:])))
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+
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+ # sample w.r.t. distribution
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+ def distr_sample(rng, pars, n, choice = 2):
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+ rvs = eval(distr_str).rvs
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+ if choice in [0,1]: return rvs( **parse_pars(pars), size = n, random_state = rng)
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+ return np.concatenate((rvs(**parse_pars(pars), size = n[0], random_state = rng),
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+ rvs(**parse_pars(pars[n_pars:]), size = n[1], random_state = rng)))
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+
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+ return log_pdfs, distr_sample
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+
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"""
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Bayesian model describing conditional probability
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