Scott Linderman (@slinderman)
github

slinderman

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Top repositories

1

pyhawkes

Python framework for inference in Hawkes processes.
Python
219
star
2

stats320

STATS320: Statistical Methods for Neural Data Analysis
Jupyter Notebook
191
star
3

recurrent-slds

Recurrent Switching Linear Dynamical Systems
Python
91
star
4

pypolyagamma

Fast C code for sampling Polya-gamma random variates. Builds on Jesse Windle's BayesLogit library.
C++
79
star
5

pyglm

Interpretable neural spike train models with fully-Bayesian inference algorithms
Python
47
star
6

theano_pyglm

Generalized linear models for neural spike train modeling, in Python! With GPU-accelerated fully-Bayesian inference, MAP inference, and network priors.
Python
46
star
7

stats215

TeX
29
star
8

stats271sp2021

Material for STATS271: Applied Bayesian Statistics (Spring 2021)
Jupyter Notebook
26
star
9

stats305c

STATS305C: Applied Statistics III (Spring, 2023)
Jupyter Notebook
23
star
10

thesis

My PhD Thesis
TeX
21
star
11

pyhsmm_spiketrains

Code for fitting neural spike trains with nonparametric hidden Markov and semi-Markov models built upon mattjj's PyHSMM framework.
Python
12
star
12

graphistician

Generative random network models and Bayesian inference algorithms
Python
10
star
13

tdlds

Reducing the temporal-difference learning theory of dopamine to a linear dynamical system
Jupyter Notebook
9
star
14

gslrandom

Cython wrapper for GSL random number generators
Python
7
star
15

stats305b

STATS 305B: Applied Statistics II. Models and Algorithms for Discrete Data.
Jupyter Notebook
6
star
16

cs281sec09

Graph models with MCMC
Python
5
star
17

neymanscott

Bayesian inference for Neyman-Scott processes
Jupyter Notebook
4
star
18

ml4nd

Machine Learning Methods for Neural Data Analysis
Jupyter Notebook
4
star
19

cython_openmp_mwe

Minimum working example of OpenMP with Cython
Python
2
star
20

birkhoff

Reparametrizing the Birkhoff Polytope
Jupyter Notebook
2
star
21

eigenglm

C++
2
star
22

dpriv_mcmc

Python
1
star
23

torchhmm

Pytorch extension to compute gradients through HMM message passing
Python
1
star