slinderman/dpriv_mcmc

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Created over 10 years ago
Updated over 10 years ago

slinderman/dpriv_mcmc

slinderman

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pyhawkes

Python framework for inference in Hawkes processes.

stats320

STATS320: Statistical Methods for Neural Data Analysis

Jupyter Notebook

recurrent-slds

Recurrent Switching Linear Dynamical Systems

pypolyagamma

Fast C code for sampling Polya-gamma random variates. Builds on Jesse Windle's BayesLogit library.

pyglm

Interpretable neural spike train models with fully-Bayesian inference algorithms

theano_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)

Jupyter Notebook

stats305c

STATS305C: Applied Statistics III (Spring, 2023)

Jupyter Notebook

thesis

pyhsmm_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 algorithms

tdlds

Reducing the temporal-difference learning theory of dopamine to a linear dynamical system

Jupyter Notebook

gslrandom

Cython wrapper for GSL random number generators

stats305b

STATS 305B: Applied Statistics II. Models and Algorithms for Discrete Data.

Jupyter Notebook

cs281sec09

Graph models with MCMC

neymanscott

Bayesian inference for Neyman-Scott processes

Jupyter Notebook

ml4nd

Machine Learning Methods for Neural Data Analysis

Jupyter Notebook

cython_openmp_mwe

Minimum working example of OpenMP with Cython

birkhoff

Reparametrizing the Birkhoff Polytope

Jupyter Notebook

eigenglm

torchhmm

Pytorch extension to compute gradients through HMM message passing