pymc

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 updating some calculations where I used pymc2 to pymc3 and I'm having some problems with samplers behavior when I have some discrete random variables on my model. As an example, consider the following model using pymc2: import pymc as pm N = 100 data = 10 p = pm.Beta('p', alpha=1.0, beta=1.0) q = pm.Beta('q', alpha=1.0, beta=1.0) A = pm.Binomial('A', N, p) X = pm.Binomial('x', A, q, observed=True, value=data) It's not really representative of anything, it's just a model where one of the unobserved variables is discrete. When I sample this model with pymc2 I get the following results: mcmc

I am trying to combine cvxopt (an optimization solver) and PyMC (a sampler) to solve convex stochastic optimization problems . For reference, installing both packages with pip is straightforward: pip install cvxopt pip install pymc Both packages work independently perfectly well. Here is an example of how to solve an LP problem with cvxopt : # Testing that cvxopt works from cvxopt import matrix, solvers # Example from http://cvxopt.org/userguide/coneprog.html#linear-programming c = matrix([-4., -5.]) G = matrix([[2., 1., -1., 0.], [1., 2., 0., -1.]]) h = matrix([3., 3., 0., 0.]) sol = solvers

This is perhaps a silly question. I'm trying to fit data to a very strange PDF using MCMC evaluation in PyMC. For this example I just want to figure out how to fit to a normal distribution where I manually input the normal PDF. My code is: data = []; for count in range(1000): data.append(random.gauss(-200,15)); mean = mc.Uniform('mean', lower=min(data), upper=max(data)) std_dev = mc.Uniform('std_dev', lower=0, upper=50) # @mc.potential # def density(x = data, mu = mean, sigma = std_dev): # return (1./(sigma*np.sqrt(2*np.pi))*np.exp(-((x-mu)**2/(2*sigma**2)))) mc.Normal('process', mu=mean, tau

I am trying to figure out how to properly make a discrete state Markov chain model with pymc . As an example (view in nbviewer ), lets make a chain of length T=10 where the Markov state is binary, the initial state distribution is [0.2, 0.8] and that the probability of switching states in state 1 is 0.01 while in state 2 it is 0.5 import numpy as np import pymc as pm T = 10 prior0 = [0.2, 0.8] transMat = [[0.99, 0.01], [0.5, 0.5]] To make the model, I make an array of state variables and an array of transition probabilities that depend on the state variables (using the pymc.Index function)

Given a posterior p(Θ|D) over some parameters Θ, one can define the following: Highest Posterior Density Region: The Highest Posterior Density Region is the set of most probable values of Θ that, in total, constitute 100(1-α) % of the posterior mass. In other words, for a given α, we look for a p * that satisfies: and then obtain the Highest Posterior Density Region as the set: Central Credible Region: Using the same notation as above, a Credible Region (or interval) is defined as: Depending on the distribution, there could be many such intervals. The central credible interval is defined as a

I'm using PYMC 2.3.4. I found terrific. Now I would like to do some goodness of the fit and plot discrpancies how shown in section 7.3 of the documentation ( https://pymc-devs.github.io/pymc/modelchecking.html ) In the documentation they say that you need 3 inputs for the discrepancy plot x: the data x_sim: the posterior distribution sample x_exp:expected value I can understand the first two but not the third This the code Sero=[0,1,4,2,2,7,13,17,90] Pop=[ 15,145,170,132,107,57,68,57,251] for i in range(len(Pop)): prob[i] = pymc.Uniform(`prob_%i' % i, 0,1.0) serobservation=pymc.Binomial(