from RunDEMC import DE
from RunDEMC.hierarchy import Hierarchy, HyperParam
import numpy as np
from joblib import Parallel, delayed
# generate the data
n_jobs = 1
nsubj = 4
nobs = 1000
mu = 1.0
sigma = .5
dmod_mean = dists.normal(mu, sigma)
alpha = 4
beta = 10
dmod_std = dists.invgamma(alpha, beta)
data = {}
for s in range(nsubj):
# draw a mean and std from hypers
smean = dmod_mean.rvs()
sstd = np.sqrt(dmod_std.rvs())
dmod = dists.normal(smean, sstd)
data[s] = {'mean': smean,
'std': sstd,
'obs': dmod.rvs(nobs)}
# set up model evaluation
def eval_prop(indiv, subj_id):
# get the true pdf for those params
the supplied distribution.
log_prior: convenience method for looping over params for a proposal
and calling each param's pdf to get the log_like for that param.
"""
from RunDEMC import Model, HyperPrior, Hierarchy, Param, dists
# set up the hyper priors
h_alpha = HyperPrior(name='h_alpha',
dist=dists.normal,
params=[
Param(name='mu', prior=dists.normal(1, .5)),
Param(name='sigma', prior=dists.invgamma(4, 10))
])
h_beta = HyperPrior(name='h_beta',
dist=dists.normal,
params=[
Param(name='mu', prior=dists.normal(1, .5)),
Param(name='sigma', prior=dists.invgamma(4, 10))
])
# set up lower level (i.e., subject)
def subj_like(pop, *args):
return np.log(dists.beta(pop[:, 0], pop[:, 1]).pdf(args[0]))
the supplied distribution.
log_prior: convenience method for looping over params for a proposal
and calling each param's pdf to get the log_like for that param.
"""
from RunDEMC import Model, HyperPrior, Hierarchy, Param, dists
# set up the hyper priors
h_alpha = HyperPrior(name='h_alpha',
dist=dists.normal,
params=[Param(name='mu', prior=dists.normal(1,.5)),
Param(name='sigma', prior=dists.invgamma(4,10))])
h_beta = HyperPrior(name='h_beta',
dist=dists.normal,
params=[Param(name='mu', prior=dists.normal(1,.5)),
Param(name='sigma', prior=dists.invgamma(4,10))])
# set up lower level (i.e., subject)
def subj_like(pop, *args):
return np.log(dists.beta(pop[:,0],pop[:,1]).pdf(args[0]))
submods = [Model(name='subj_%s'%s,
params=[Param(name='alpha', prior=h_alpha),
Param(name='beta', prior=h_beta)],
like_fun=subj_like,
like_args=(sdat[s],),
from RunDEMC import DE
from RunDEMC.hierarchy import Hierarchy,HyperParam
import numpy as np
from joblib import Parallel, delayed
# generate the data
n_jobs = 1
nsubj = 4
nobs = 1000
mu=1.0
sigma=.5
dmod_mean = dists.normal(mu,sigma)
alpha=4
beta=10
dmod_std = dists.invgamma(alpha,beta)
data = {}
for s in range(nsubj):
# draw a mean and std from hypers
smean = dmod_mean.rvs()
sstd = np.sqrt(dmod_std.rvs())
dmod = dists.normal(smean,sstd)
data[s] = {'mean': smean,
'std': sstd,
'obs': dmod.rvs(nobs)}
# set up model evaluation
def eval_prop(indiv, subj_id):
# get the true pdf for those params
mod = dists.normal(indiv[0],np.exp(indiv[1]))
pdf = mod.pdf(data[subj_id]['obs'])
prior=dists.CustomDist(
pdf=lambda x: np.exp(-1.5 * np.log(1 + x**2)),
rvs=dists.laplace(0, 5).rvs))
# Fixed noise across subjects
# Using a custom Jeffreys' prior
sigma = Param(name='sigma',
display_name=r'$\sigma$',
prior=dists.CustomDist(pdf=lambda x: np.exp(-np.log(x)),
rvs=dists.dists.invgamma(1, 1).rvs))
# Hyperprior over intercept using a normal distribution
halpha = HyperPrior('alpha',
dists.normal,
params=[
Param(name='mu', prior=dists.uniform(-50, 50)),
Param(name='sig', prior=dists.invgamma(1, 1))
])
# set up the submodels for each participant
smods = []
for j in range(nsubj):
# Append a new model, note the use of the hyperprior for setting
# up the intercept param, and the fixed beta and sigma across
# participants
smods.append(
Model(name=str(j),
params=[
Param(name='alpha', display_name=r'$\alpha$', prior=halpha),
beta, sigma
],
like_fun=subj_like,
