pymc3

To learn PyMC, I'm trying to do a simple Hidden Markov Model as shown below: with pymc3.Model() as hmm: # Transition "matrix" a_t = np.ones(num_states) T = [pymc3.Dirichlet('T{0}'.format(i), a = a_t, shape = num_states) for i in xrange(num_states)] # Emission "matrix" a_e = np.ones(num_emissions) E = [pymc3.Dirichlet('E{0}'.format(i), a = a_e, shape = num_emissions) for i in xrange(num_states)] # State models p0 = np.ones(num_states) / num_states # No shape, so each state is a scalar tensor states = [pymc3.Categorical('s0', p = p0)] emissions = [pymc3.Categorical('z0', p = ifelse(eq(states[0],

I've recently started learning pymc3 after exclusively using emcee for ages and I'm running into some conceptual problems. I'm practising with Chapter 7 of Hogg's Fitting a model to data . This involves a mcmc fit to a straight line with arbitrary 2d uncertainties. I've accomplished this quite easily in emcee , but pymc is giving me some problems. It essentially boils down to using a multivariate gaussian likelihood. Here is what I have so far. from pymc3 import * import numpy as np import matplotlib.pyplot as plt size = 200 true_intercept = 1 true_slope = 2 true_x = np.linspace(0, 1, size) #

pymc.__version__ = '3.0' theano.__version__ = '0.6.0.dev-RELEASE' I'm trying to use PyMC3 with a complex likelihood function: First question: Is this possible? Here's my attempt using Thomas Wiecki's post as a guide: import numpy as np import theano as th import pymc as pm import scipy as sp # Actual data I'm trying to fit x = np.array([52.08, 58.44, 60.0, 65.0, 65.10, 66.0, 70.0, 87.5, 110.0, 126.0]) y = np.array([0.522, 0.659, 0.462, 0.720, 0.609, 0.696, 0.667, 0.870, 0.889, 0.919]) yerr = np.array([0.104, 0.071, 0.138, 0.035, 0.102, 0.096, 0.136, 0.031, 0.024, 0.035]) th.config.compute_test

I'd like to use pymc3 to estimate unknown parameters and states in a Hodgkin Huxley neuron model. My code in pymc is based off of http://healthyalgorithms.com/2010/10/19/mcmc-in-python-how-to-stick-a-statistical-model-on-a-system-dynamics-model-in-pymc/ and executes reasonably well. #parameter priors @deterministic def HH(priors in here) #model equations #return numpy arrays that somehow contain the probability distributions as elements return V,n,m,h #Make V deterministic in one line. Seems to be the magic that makes this work. V = Lambda('V', lambda HH=HH: HH[0]) #set up the likelihood A =

I'm trying to create the model shown below with PyMC 3 but can't figure out how to properly map probabilities to the observed data with a lambda function. import numpy as np import pymc as pm data = np.array([[0, 0, 1, 1, 2], [0, 1, 2, 2, 2], [2, 2, 1, 1, 0], [1, 1, 2, 0, 1]]) (D, W) = data.shape V = len(set(data.ravel())) T = 3 a = np.ones(T) b = np.ones(V) with pm.Model() as model: theta = [pm.Dirichlet('theta_%s' % i, a, shape=T) for i in range(D)] z = [pm.Categorical('z_%i' % i, theta[i], shape=W) for i in range(D)] phi = [pm.Dirichlet('phi_%i' % i, b, shape=V) for i in range(T)] w = [pm

I'm doing some pymc3 and I would like to create custom Stochastics, however there doesn't seem to be a lot documentation about how it's done. I know how to use the as_op way , however apparently that makes it impossible to use the NUTS sampler, in which case I don't see the advantage of pymc3 over pymc. The tutorial mentions that it can be done by inheriting from theano.Op. But can anyone show me how that would work (I'm still getting started on theano)? I have two Stochastics that I want to define. The first one should be easier, it's an N dimension vector F that has only constant parent

I'm attempting to write a custom Theano Op which numerically integrates a function between two values. The Op is a custom likelihood for PyMC3 which involves the numerical evaluation of some integrals. I can't simply use the @as_op decorator as I need to use HMC to do the MCMC step. Any help would be much appreciated, as this question seems to have come up several times but has never been solved (e.g. https://stackoverflow.com/questions/36853015/using-theano-with-numerical-integration , Theano: implementing an integral function ). Clearly one solution would be to write a numerical integrator