Scott Linderman

pyhawkes. Python framework for inference in Hawkes processes.

stats320. STATS320: Statistical Methods for Neural Data Analysis

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. TeX

stats305c. STATS305C: Applied Statistics III (Spring, 2023)

stats271sp2021. Material for STATS271: Applied Bayesian Statistics (Spring 2021)

thesis. My PhD Thesis

ml4nd. Machine Learning Methods for Neural Data Analysis

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

stats305b. STATS 305B: Applied Statistics II. Models and Algorithms for Discrete Data.

gslrandom. Cython wrapper for GSL random number generators

cs281sec09. Graph models with MCMC

neymanscott. Bayesian inference for Neyman-Scott processes

course-content. NMA Computational Neuroscience course

birkhoff. Reparametrizing the Birkhoff Polytope

CaImAn. Computational toolbox for large scale Calcium Imaging Analysis, including movie handling, motion correction, source extraction, spike deconvolution and result visualization.

numpyro. Probabilistic programming with NumPy powered by JAX for autograd and JIT compilation to GPU/TPU/CPU.

eigenglm. C++

cython_openmp_mwe. Minimum working example of OpenMP with Cython

torchhmm. Pytorch extension to compute gradients through HMM message passing