pymc3 | Probabilistic Programming in Python : Bayesian Modeling | Analytics library
kandi X-RAY | pymc3 Summary
kandi X-RAY | pymc3 Summary
Probabilistic Programming in Python: Bayesian Modeling and Probabilistic Machine Learning with Aesara
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pymc3 Examples and Code Snippets
Lines of Code : 4License : Strong Copyleft (CC BY-SA 4.0)
Copyimport theano.tensor as tt
tt.dot(a,b)
import pymc3 as pm
import scipy.stats
import numpy as np
# declare observed data above to check later
data = np.random.randn(100)
# that's the parameter value for which you want the unnormalized log posterior density
fixed_mu = 0
with pm> help(pm.find_hessian)
find_hessian(point, vars=None, model=None)
Returns Hessian of logp at the point passed.
Parameters
----------
model: Model (optional if in `with` context)
point: dict
vars: list
def normpdf(y, mu, sigma):
return pm.math.sum((1 / sigma) * pm.math.exp(pow(y - mu, 2) / pow(sigma, 2)))
def logp(mu, sigma, w, y):
mu1 = mu[0]
mu2 = mu[1]
sigma1 = sigma[0]
sigma2 = sigma[1]
return np.log(w * normimport theano
y_tensor = theano.shared(train.y.values.astype('float64'))
x_tensor = theano.shared(train.x.values.astype('float64'))
map_tensor_batch = {y_tensor: pm.Minibatch(train.y.values, 100),
x_tensor: pm.Minibatwith model:
...
samp = fit.sample(1500)
idata = az.from_pymc3(samp)
az.summary(idata.posterior[["b"]].sel({"b_dim_0": [0]}))
c = 2.0
with pm.Model() as my_model:
a = pm.Normal("a", -1, 0.1)
b = pm.Normal("b", 15.0, 2.0)
Y_mu = pm.Deterministic("Y_mu", a + b + c)
Y_sigma = pm.Normal('Y_sigma', mu=1, sd=1)
Y = pm.Normal("Y", mu=Y_mu,tn1 = pm.TruncatedNormal.dist(mu=mu1, tau=tau1, lower=0, upper=1)
P[x=0|Normal[0,1]] = 1/sqrt(2*pi) = 0.3989422804014327
P[x=0| Zero ] = 1
with pm.Model() as model:
norm = pm.Normal.dist(0.0, 1.0)
pseudo_zero = pm.Laplace.dist(0.0, 1e-16)
components = [norm,# starting out with a fresh environment
conda create -n mypm3env python=3.8 mkl-service libpython m2w64-toolchain scipy matplotlib pandas
# install PyMC3 through pip
pip install pymc3
Community Discussions
Trending Discussions on pymc3
QUESTION
I have started to learn PYMC3. i am trying to write a simple matrix multiplication using PYMC3. basically would like to learn and understand how the arithmetic operations can be done in PYMC3.
Below is my code,
...ANSWER
Answered 2022-Mar-16 at 17:37Setting aside that the model here is not clear, we can still answer how matrix multiplication should be done when working with PyMC3 RandomVariable objects. Namely, RV's in PyMC3 are theano.tensor.TensorVaribale objects, and therefore, should use the theano.tensor.dot method to matrix multiply them. E.g.,
QUESTION
I am using pymc3 package for Bayesian modelling in Python. Below is my code
ANSWER
Answered 2022-Mar-02 at 08:59The logp method should give you the unnormalized log posterior, i.e. the (log) numerator of Bayes' rule. Recall that the posterior is proportional to the product of the prior and the likelihood and the log posterior is proportional to the sum of the log prior and the log likelihood. I.e. if you want to reproduce the output of logp you also have to consider the likelihood, not only the prior. You can check it like that:
QUESTION
I want to define an inequality constraint for a PYMC3 model. I found this post about defining an equality constraint (i.e., a+b1+b2=1) using pm.Potential. Does anyone know how to change that equality constraint into an inequality constraint like 0.9<1? Thanks!
...ANSWER
Answered 2022-Jan-17 at 16:05The post you mention uses pm.math.eq which stands for "equal". There are also pm.math.lt (lower than) and pm.math.le (lower or equal than). See the pymc3.math documentation
QUESTION
I have a sample from PyMC3 and I'm trying to get a cumulative probability from it, e.g. P(X < 0). I currently use this:
...ANSWER
Answered 2022-Jan-12 at 11:32You could approximate the CDF with a kernel density estimate, but I am not convinced that this is better than your current approach:
QUESTION
I'm trying to include a black box likelihood function in a pymc3 model. This likelihood function just takes a vector of parameter values and returns the likelihood (all data is already included in the function).
So far I've been following this guide and have modified the code as follows to accommodate the fact my model only has one parameter k.
...ANSWER
Answered 2021-Nov-05 at 09:42As per the comments I checked out this thread and discovered that pm.potential really was the cleanest way to achieve black-box likelihood. Modifying the code above as follows did the trick:
QUESTION
data source: https://catalog.data.gov/dataset/nyc-transit-subway-entrance-and-exit-data
I tried looking for a similar problem but I can't find an answer and the error does not help much. I'm kinda frustrated at this point. Thanks for the help. I'm calculating the closest distance from a point.
...ANSWER
Answered 2021-Oct-11 at 14:21geopandas 0.10.1
- have noted that your data is on kaggle, so start by sourcing it
- there really is only one issue
shapely.geometry.MultiPoint()constructor does not work with a filtered series. Pass it a numpy array instead and it works. - full code below, have randomly selected a point to serve as
gpdPoint
QUESTION
I have a website where I document a list of installed pythonic libraries.
For each library, I want to have available:
- The name of the library (obviously)
- A link to the documentation for the library (because documentation is useful)
- A brief description of the library (so people can quickly see what the library does)
- The currently installed version (to stop people asking me "Are you using version x.y?")
My current solution is to use the name as the text of a link, href'd to its documentation, and accept that the version & description are supplementary information, and can be made available to the user using a tool-tip - so they can sit in a title attribute
Example:
...ANSWER
Answered 2021-Sep-08 at 08:25Use focus-within rather than focus
QUESTION
I am trying to determine the parameters mu1, mu2, sigma1, sigma2, and w of a bimodal distribution using pymc3. I use the following code:
...ANSWER
Answered 2021-Sep-01 at 18:11Using pdb I stepped through and found that the error was on this line:
return np.log(w * norm.pdf(y, mu1, sigma1) + (1-w) * norm.pdf(y, mu2, sigma2)).sum()
because scipy.stats.norm doesn't know how to deal with pymc/Theano objects. While I'm sure there's an easy way to get a normal density from pymc3 objects, it's also straightforward to define a function to calculate it ourselves:
QUESTION
I’m trying to use PyMC3 Minibatch ADVI for Bayesian Regression. The pm.fit function throws the following error and I’m not sure how to fix it.
It says that the ‘str’ object has no attribute ‘type’. What is any ‘str’ object from the error message here? I’ve mapped float tensors for more_replacements to the best of what I know.
...ANSWER
Answered 2021-May-31 at 17:34The blog post you are working from shows
QUESTION
I am estimating a model using the pyMC3 library in python. In my "real" model, there are four parameter arrays, two of which have over 170,000 parameters in them. Summarising this array of parameters is too computationally intensive on my computer. I have been trying to figure out if the summary function in arviz will allow me to only summarise one (or a small number) of parameters in the array. Below is a reprex where the same problem is present, though the model is a lot simpler. In the linear regression model below, the parameter array b has three parameters in it b[0], b[1], b[2]. I would like to know how to get the summary for just b[0] and b[1] or alternatively for just a single parameter, e.g., b[0].
ANSWER
Answered 2021-May-28 at 15:34To use coords for this, you need to update to the development (which will still show 0.11.2 but has the code from github or any >0.11.2 release) version of ArviZ. Until 0.11.2, the coords argument in summary was not used to subset the data (like it did in all plotting functions) but instead it was only taken into account if the input was not already InferenceData in which case it was passed to the converter.
With older versions, you need to use xarray to subset the data before passing it to summary. Therefore you need to explicitly convert the trace to inferencedata beforehand. In the example above it would look like:
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Install pymc3
You can use pymc3 like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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