def likelihood(mcmc, position):
mcmc.simulate(position=position)
#sigma = mcmc.cur_params(position)[-1]
nbd = mcmc.solver.yexpr['NBD']
# The value of 0.085 for the SD was obtained by examining the residuals
# at a maximum posterior fit
err = np.sum((mcmc.data - nbd)**2 / (2 * 0.085 ** 2))
#err = np.sum((mcmc.data - nbd)**2 / (2 * sigma ** 2))
return err
def likelihood(mcmc, position):
mcmc.simulate(position=position)
#sigma = mcmc.cur_params(position)[-1]
nbd = mcmc.solver.yexpr['NBD']
dr = mcmc.solver.yexpr['Tb']
dr_sd = mcmc.dr_data * 0.03
nbd_sd = mcmc.nbd_data * 0.03
err = np.sum((mcmc.nbd_data - nbd)**2 / (2 * nbd_sd ** 2)) + \
np.sum((mcmc.dr_data - dr)**2 / (2 * dr_sd ** 2))
#err = np.sum((mcmc.dr_data - dr)**2 / (2 * dr_sd ** 2))
return err
def likelihood(mcmc, position):
yout = mcmc.simulate(position, observables=True)
# TODO Need to be able to get the indices from the model so that
# they're not hardcoded
params = mcmc.cur_params(position)
err = 0
for data_index, obs_name in zip(data_indices, self.nbd_observables):
timecourse = ((yout[obs_name] /
mcmc.options.model.parameters['Bax_0'].value)
* params[3])
err += np.sum((self.nbd_avgs[data_index] - timecourse)**2 /
(2 * self.nbd_stds[data_index]**2))
return err
def likelihood(mcmc, position):
"""The likelihood function."""
err = 0
for bax_conc in mcmc.data.columns:
# Get the data for this concentration
tc = mcmc.data[bax_conc]
y_data = np.array(tc[:,'MEAN'])
time = np.array(tc[:,'TIME'])
mcmc.solver.tspan = time # set the time span
# Get the simulated data for this concentration
mcmc.options.model.parameters['Bax_0'].value = bax_conc
x = mcmc.simulate(position=position, observables=True)
avg_pores = x['pores']/ \
mcmc.options.model.parameters['Vesicles_0'].value
y_mod = 1 - np.exp(-avg_pores)
# Calculate the error, accounting for the SD at this
# concentration.
# Skip the first timepoint--the SD is 0 (due to normalization)
# and hence gives nan when calculating the error.
err += np.sum(((y_data[1:] - y_mod[1:])**2) / \
(2 * (np.array(tc[:,'SD'][1:]) ** 2)))
return err
def likelihood(mcmc, position):
mcmc.simulate(position=position)
nbd = mcmc.solver.yexpr['NBD']
err = np.sum((mcmc.data - nbd)**2 / (2 * ((0.02 * mcmc.data)**2)))
return err
