pymc

I want to run some data science algorithms using Markov Chain Monte Carlo for Bayesian analysis and am trying to install PyMC but am frustratingly getting this error... File "C:\Anaconda\lib\site-packages\numpy\distutils\fcompiler\gnu.py", line 333, in get_libraries raise NotImplementedError("Only MS compiler supported with gfortran on win64") NotImplementedError: Only MS compiler supported with gfortran on win64 Why would this happen and what can I do to solve it that doesnt require me hacking up python and apparently numpy that might screw other things up at a later time? It turns out if I

I'm trying to estimate the rate of a Poisson process where the rate varies over time using the maximum a posteriori estimate. Here's a simplified example with a rate varying linearly (λ = ax+b) : import numpy as np import pymc # Observation a_actual = 1.3 b_actual = 2.0 t = np.arange(10) obs = np.random.poisson(a_actual * t + b_actual) # Model a = pymc.Uniform(name='a', value=1., lower=0, upper=10) b = pymc.Uniform(name='b', value=1., lower=0, upper=10) @pymc.deterministic def linear(a=a, b=b): return a * t + b r = pymc.Poisson(mu=linear, name='r', value=obs, observed=True) model = pymc.Model(

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 read the following paper( http://www3.stat.sinica.edu.tw/statistica/oldpdf/A10n416.pdf ) where they model the variance-covariance matrix Σ as: Σ = diag(S)*R*diag(S) (Equation 1 in the paper) S is the k×1 vector of standard deviations, diag(S) is the diagonal matrix with diagonal elements S, and R is the k×k correlation matrix. How can I implement this using PyMC ? Here is some initial code I wrote: import numpy as np import pandas as pd import pymc as pm k=3 prior_mu=np.ones(k) prior_var=np.eye(k) prior_corr=np.eye(k) prior_cov=prior_var*prior_corr*prior_var post_mu = pm.Normal("returns"

Say I want to put a custom prior on two variables a and b in PyMC, e.g.: p(a,b)∝(a+b)^(−5/2) (for the motivation behind this choice of prior, see this answer ) Can this be done in PyMC? If so how? As an example, I would like to define such prior on a and b in the model below. import pymc as pm # ... # Code that defines the prior: p(a,b)∝(a+b)^(−5/2) # ... theta = pm.Beta("prior", alpha=a, beta=b) # Binomials that share a common prior bins = dict() for i in xrange(N_cities): bins[i] = pm.Binomial('bin_{}'.format(i), p=theta,n=N_trials[i], value=N_yes[i], observed=True) mcmc = pm.MCMC([bins, ps]