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pyhawkes
Python framework for inference in Hawkes processes.stats320
STATS320: Statistical Methods for Neural Data Analysisrecurrent-slds
Recurrent Switching Linear Dynamical Systemspyglm
Interpretable neural spike train models with fully-Bayesian inference algorithmstheano_pyglm
Generalized linear models for neural spike train modeling, in Python! With GPU-accelerated fully-Bayesian inference, MAP inference, and network priors.stats215
stats271sp2021
Material for STATS271: Applied Bayesian Statistics (Spring 2021)stats305c
STATS305C: Applied Statistics III (Spring, 2023)thesis
My PhD Thesispyhsmm_spiketrains
Code for fitting neural spike trains with nonparametric hidden Markov and semi-Markov models built upon mattjj's PyHSMM framework.graphistician
Generative random network models and Bayesian inference algorithmstdlds
Reducing the temporal-difference learning theory of dopamine to a linear dynamical systemgslrandom
Cython wrapper for GSL random number generatorsstats305b
STATS 305B: Applied Statistics II. Models and Algorithms for Discrete Data.cs281sec09
Graph models with MCMCneymanscott
Bayesian inference for Neyman-Scott processesml4nd
Machine Learning Methods for Neural Data Analysiscython_openmp_mwe
Minimum working example of OpenMP with Cythonbirkhoff
Reparametrizing the Birkhoff Polytopeeigenglm
dpriv_mcmc
torchhmm
Pytorch extension to compute gradients through HMM message passingLove Open Source and this site? Check out how you can help us