csetraynor Goto Github PK

2018-stancon-notebook

Notebook submission for StanCon 2018

additive-gps

Source for experiments in the Additive Gaussian process paper, as well as extensions relating to dropout.

advancessurvprojectpkuk

AdvancesSurvProjectPKUK

aeidm

Adverse events, Illness, Death multi-state Models

aeim

Adverse events, Illness, multi-state survival Models

amazon-sagemaker-script-mode

Amazon SageMaker Script Mode examples

amputation_with_ampute

annoy

Approximate Nearest Neighbors in C++/Python optimized for memory usage and loading/saving to disk

autoencoder-gan

Variational Autoencoder and Generative Adversarial Network

autoimpute

Python package for Imputation Methods

autoprognosis

Codebase for "AutoPrognosis: Automated Clinical Prognostic Modeling via Bayesian Optimization", ICML 2018.

awesome-causality-algorithms

An index of algorithms for learning causality with data

awesome-cpp

A curated list of awesome C/C++ frameworks, libraries, resources, and shiny things. Inspired by awesome-... stuff.

awesome-datascience

:memo: An awesome Data Science repository to learn and apply for real world problems.

awesome-machine-learning

A curated list of awesome Machine Learning frameworks, libraries and software.

awesome-ros2

The Robot Operating System Version 2.0 is awesome!

baselines

OpenAI Baselines: high-quality implementations of reinforcement learning algorithms

bayeshmm

Full Bayesian Inference for Hidden Markov Models

bayesian_notes

bayesiancoxfrailtycomparision

bayesplot

bayesplot R package for plotting Bayesian models

bayesx

:exclamation: This is a read-only mirror of the CRAN R package repository. BayesX — R Utilities Accompanying the Software Package BayesX

bayesyoungmeet

bda_py_demos

Bayesian Data Analysis demos for Python

bda_r_demos

Bayesian Data Analysis demos for R

biocintro

Course material for introductory R / Bioconductor courses

bioconductorschool

biomed-plash

bionetgen

Rule-based modeling framework

biostan

Materials for BioC-2016 workshop entitled "Introduction to Bayesian Inference using Stan with Applications to Cancer Genomics"