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@springcoil
springcoil / pymc3_normalizing_flows.ipynb
Created December 16, 2016 23:11
Pymc3 normalizing flows WIP
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@AustinRochford
AustinRochford / pydata-nyc-2017-nba-fouls-python.ipynb
Last active December 22, 2017 16:22
PyData NYC 2017 Understanding NBA Foul Calls with Python
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@aseyboldt
aseyboldt / sensitivity.ipynb
Last active January 18, 2018 11:47
A *very* basic and possibly wrong implementation of https://github.com/rgiordan/StanSensitivity in pymc3
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"""Sampling parameters of a lorenz attractor.
The forward pass integrates the lorenz attractor ODE system using
tt.scan with a Runge-Kutta integrator. The predicted high-resolution
timecourse is interpolated down so it can be compared to low-density
observations.
"""
import abc
import numpy
import pymc3
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@bwengals
bwengals / gp_poisson.ipynb
Created March 19, 2017 10:13
poisson gp regression of old faithful histogram data fitted with pymc3
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@AustinRochford
AustinRochford / boston-bayesians-2017-bayes-bandits.ipynb
Last active October 16, 2019 08:41
Boston Bayesians 2017 - Two Years of Bayesian Bandits for E-Commerce
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@twiecki
twiecki / dask_sparse_corr.py
Created August 17, 2018 11:26
Compute large, sparse correlation matrices in parallel using dask.
import dask
import dask.array as da
import dask.dataframe as dd
import sparse
@dask.delayed(pure=True)
def corr_on_chunked(chunk1, chunk2, corr_thresh=0.9):
return sparse.COO.from_numpy((np.dot(chunk1, chunk2.T) > corr_thresh))
def chunked_corr_sparse_dask(data, chunksize=5000, corr_thresh=0.9):
@AustinRochford
AustinRochford / pymc3_bsplines.ipynb
Last active September 15, 2022 21:08
PyMC3 BSplines
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@bwengals
bwengals / DTC_latent.ipynb
Created April 3, 2018 01:56
DTC for latent GP, non-normal likelihoods
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