pymc

I tried to port simple survival model from here (the first one in introduction) form PyMC 2 to PyMC 3. However, I didn't find any equivalent to "observed" decorator and my attempt to write a new distribution failed. Could someone provide an example how is this done in PyMC 3? This is a tricky port, and requires three new concepts: Use of the theano tensor Use of the DensityDist Passing a dict as observed This code provides the equivalent model as the PyMC2 version you linked to above: import pymc3 as pm from pymc.examples import melanoma_data as data import theano.tensor as t times = data.t #

How can I define a custom likelihood in PyMC3? In PyMC2, I could use @pymc.potential . I tried to use pymc.Potential in PyMC3, however, it seems that boolean operations cannot be applied to the parameters (I get an error like this when I do that). For example, following code does not work: from pymc import * with Model() as model: x = Normal('x', 1, 1) def z(u): if u > 0: #comparisons like this are not supported # if theano.tensor.lt(0,u): this is how comparison should be done return u ** 2 return -u**3 x2 = Potential('x2', z(x)) start = model.test_point h = find_hessian(start) step =

I have some observational data for which I would like to estimate parameters, and I thought it would be a good opportunity to try out PYMC3. My data is structured as a series of records. Each record contains a pair of observations that relate to a fixed one hour period. One observation is the total number of events that occur during the given hour. The other observation is the number of successes within that time period. So, for example, a data point might specify that in a given 1 hour period, there were 1000 events in total, and that of those 1000, 100 were successes. In another time period,

Is it possible to incrementally update a model in pyMC3. I can currently find no information on this. All documentation is always working with a priori known data. But in my understanding, a Bayesian model also means being able to update a belief. Is this possible in pyMC3? Where can I find info in this? Thank you :) Following @ChrisFonnesbeck's advice, I wrote a small tutorial notebook about incremental prior updating. It can be found here: https://github.com/pymc-devs/pymc3/blob/master/docs/source/notebooks/updating_priors.ipynb Basically, you need to wrap your posterior samples in a custom

I am trying to fit some data with a Gaussian (and more complex) function(s). I have created a small example below. My first question is, am I doing it right? My second question is, how do I add an error in the x-direction, i.e. in the x-position of the observations/data? It is very hard to find nice guides on how to do this kind of regression in pyMC. Perhaps because its easier to use some least squares, or similar approach, I however have many parameters in the end and need to see how well we can constrain them and compare different models, pyMC seemed like the good choice for that. import