dailysoap/cs294m

Markov Chain Monte Carlo: Foundations & Applications

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Torm/mn2

Discovery of Markov network structures

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jonzia/ProbabilisticGraphs

Designing and training probabilistic graphical models (MATLAB).

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zdk123/pulsar

pulsar: Parallel Utilities for Lambda Selection along a Regularization Path

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chunlinli/grivet

Graphical Instrumental Variable Estimation and Testing

Language: R - Size: 10.2 MB - Last synced at: over 1 year ago - Pushed at: over 1 year ago - Stars: 2 - Forks: 0

mims-harvard/fusenet

Network inference by fusing data from diverse distributions

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simonkamronn/kvae

Kalman Variational Auto-Encoder

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sqyu/ZiDAG

Directed Graphical Models and Causal Discovery for Zero-Inflated Data.

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biips/rbiips

R package for Bayesian inference with interacting particle systems

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biips/matbiips

Matlab toolbox for Bayesian inference with interacting particle systems

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m-niemeyer/image_denoising_graphical_model

This is an example implementation of a graphical model in the domain of image denoising

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mljaniczek/ssjgl-tutorial

Tutorial for using Bayesian joint spike-and-slab graphical lasso in R

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sagarverma/LC-CFRBM

Label consistent RBM

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NM001007/Suicidal_Ideation_Detection_Using_GAT_and_GCN

In this project, three different models based on GAT, GCN and SAGE have been implemented to examine their performance on two prominent social networking platforms, namely Twitter and Reddit.

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ndt93/hop-ilp

Probabilistic planning solvers using hindsight optimization and reduction to ILP

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oskopek/pgmia-lecture-notes

Lecture notes for Probabilistic Graphical Models for Image Analysis, ETH Zurich fall 2018

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v-papageorgiou/bn_classifier

Language: Python - Size: 583 KB - Last synced at: almost 2 years ago - Pushed at: over 4 years ago - Stars: 2 - Forks: 0

uhlerlab/graphical_model_learning

Learning graphical models, with a focus on causal models and learning from interventional data.

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cvoelcker/DeepNotebooks

DeepNotebooks is an automated statistical analysis tool build on top of SPNs. They are currently being developed by Claas Völcker at the ML group at TU Darmstadt.

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mnarayan/pyclime

Python wrapper for CLIME estimators

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amoghkori/Data-Analysis-using-Algebraic-and-Geometric-Methods-in-Statistics

This project utilizes algebraic and geometric methods in statistics to analyze real-life data using R. It involves conducting various statistical tests, creating Bayesian network graphs, employing linear regression models, and evaluating model fit.

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Mephistopheles-0/Markov-Networks-for-OCR

Performing Inference in the OCR Network

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Mephistopheles-0/Bayesian-Network-for-Genetic-Inheritance

Constructing Bayesian Networks for Genetic Inheritance

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Mephistopheles-0/Bayesian-Networks-with-MATLAB

Introduction to Bayesian Networks and some applications using OCTAVE/MATLAB

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rballester/gmtorch

Graphical Modeling in PyTorch

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chunlinli/defuse

Nonlinear Causal Discovery with Confounders

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chunlinli/intdag

Estimation and inference of a directed acyclic graph with unspecified interventions.

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chunlinli/clrdag

R package for likelihood estimation and inference of a directed acyclic graph.

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javzapata/fgm

R package for Partially Separable Multivariate Functional Data and Functional Graphical Models

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savinims/DATAS_Causal_Discovery

Causal inference tutorials written as part of the Data Analysis Tools for Atmospheric Scientists (DATAS) Gateway.

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javierlorenzod/Time-Series-Analysis Fork of yezhang-xiaofan/Time-Series-Analysis

This project uses machine learning algorithms to analyze waiting time at DMV offices.

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YohannaWANG/CauchyEst

We analyze algorithms to learn Gaussian Bayesian networks with known structure up to a bounded error in total variation distance.

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lorenzoFabbri/paper-helix-multiOmics

PhD paper #1

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fredzzhang/spatially-conditioned-graphs

Official PyTorch implementation for ICCV 2021 paper "Spatially Conditioned Graphs for Detecting Human–Object Interactions"

Language: Python - Size: 3.45 MB - Last synced at: about 2 years ago - Pushed at: about 3 years ago - Stars: 55 - Forks: 11

ioangatop/machine_learning_2

Repository of UvA's lab exercises for the course "Machine Learning 2" 2019.

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juliusberner/pgm_tutorial

A short introduction to probabilistic graphical models using jupyter slides

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MauroCE/PythonBRMLtoolbox

Python 3.7 version of David Barber's MATLAB BRMLtoolbox

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lingxuez/bayes-net

Checking D-separations and I-equivalence in Bayesian Networks.

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RobertoFalconi/InteractiveGraphics

Interactive Graphics exam project. Interactive 3D renders of a cube and of a dog with WebGL.

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nbhushan/Gaussian-Graphical-Models

An introduction to graphical models in psychometrics.

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harisankar01/Airport-Feedback-Analytics-Website

A website that provides visual representations and analytics of airport feedback to figure out the areas that need to be improved to improve the qualities of the airports and airlines.

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jayantdahiya/Lorenz-System-Implementation

Implementation of Lorenz system in python(Jupyter notebook) and Processing.

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FirstHandScientist/phdthesis

PhD Thesis

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FirstHandScientist/pgm_map

probabilistic graphical model collections

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WladimirSidorenko/CRFSuite

Tree-Structured, First- and Higher-Order Linear Chain, and Semi-Markov CRFs

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biips/biips

C++ libraries for Bayesian inference with interacting particle systems

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GlooperLabs/GraphTime

A Package for Dynamic Graphical Model Estimation. Future versions in R coming soon

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kasia-kobalczyk/chain-event-graphs

R and Python scripts for my Summer 2021 undegraduate research project on Chain Event Graphs as part of the URSS scheme

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mljaniczek/spikeyglass

Package implementing Bayesian Spike-and-Slab Joint Graphical Lasso

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adallak/TSGlasso

Time Series Graphical Lasso

Language: R - Size: 19.5 KB - Last synced at: about 2 years ago - Pushed at: about 3 years ago - Stars: 1 - Forks: 1

QData/FASJEM

AISTAT 2017 Paper: A Fast and Scalable Joint Estimator for Learning Multiple Related Sparse Gaussian Graphical Models

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geodes-sms/DSMEditorGenerator

Language: Java - Size: 33.9 MB - Last synced at: about 2 years ago - Pushed at: over 3 years ago - Stars: 3 - Forks: 1

mr-easy/Restricted-Boltzmann-Machine

Python implementation of Restricted Boltzmann Machine (RBM). And an example on MNIST dataset.

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ArnoVel/structure-identification

Uses several statistical tests / algorithms on marginal / conditional distributions

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mirceamironenco/Machine_Learning_2_labs

Machine Learning 2 Course 2017 - MSc Artificial Intelligence @ UvA

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soluslab/CStrees

An R package for learning context-specific causal models, called CStrees, based on observational, or a mix of observational and interventional, data.

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melmasri/parallelDG

Parallel Bayesian inference for decomposable graphical models.

Language: Python - Size: 6.3 MB - Last synced at: over 1 year ago - Pushed at: over 1 year ago - Stars: 1 - Forks: 1

itsrainingdata/ccdrAlgorithm

Structure learning for Bayesian networks using the CCDr algorithm.

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CalvinJohn99/Blender-Weapons-Modelling

This is a repository outlining the process of building graphical models of 3 different weapons using Blender. You can find the report outlining this process within the readme file, and find the relevant .blend files for the models within the code.

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martinetoering/Machine-Learning-2

Projects from the Machine learning 2 Course on ICA, Inference in Graphical models, EM algorithm and VAE (October, 2020).

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cbg-ethz/shm

Deep hierarchical models combined with Markov random fields.

Language: Python - Size: 277 MB - Last synced at: 2 months ago - Pushed at: over 5 years ago - Stars: 2 - Forks: 2

QData/SIMULE

Machine Learning 2017 / "A constrained L1 minimization approach for estimating multiple Sparse Gaussian or Nonparanormal Graphical Models", / https://cran.r-project.org/web/packages/simule/

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chenhaotian/Bayesian-Bricks

Basic building blocks in Bayesian statistics.

Language: R - Size: 5.36 MB - Last synced at: about 2 years ago - Pushed at: over 4 years ago - Stars: 7 - Forks: 1

chaitanyamalaviya/NeuralFactorGraph

This repo contains the code for the paper Neural Factor Graph Models for Cross-lingual Morphological Tagging.

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mlindsk/ess

Eficient Stepwise Selection in Decomposable Models

Language: R - Size: 1.52 MB - Last synced at: 11 days ago - Pushed at: over 3 years ago - Stars: 1 - Forks: 1

soluslab/causalCIM

A Python package for learning and using causal networks via discrete geometry

Language: Python - Size: 101 MB - Last synced at: about 1 year ago - Pushed at: about 1 year ago - Stars: 1 - Forks: 0

ytakashina/depynd

Evaluating dependencies among random variables.

Language: Jupyter Notebook - Size: 1.62 MB - Last synced at: 28 days ago - Pushed at: about 6 years ago - Stars: 8 - Forks: 2

csinva/abide-multitask-learning

Multi-task learning of functional connectivity on the ABIDE dataset.

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aniket-agarwal1999/vGraph-Pytorch

Implementation of the paper "vGraph: A Generative Model For Joint Community Detection and Node Representational Learning" under NeurIPS Reproducibility challenge 2019

Language: Python - Size: 1.56 MB - Last synced at: about 2 months ago - Pushed at: over 5 years ago - Stars: 8 - Forks: 3

mohbenaicha/RL-and-AI---Porbabilistic-and-DQN-Wumpus-World-agents

Artificial agents and reinforcement learning term projects

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CraigKelly/grample

Sampling for Probabilistic Graphical Models

Language: Go - Size: 6.11 MB - Last synced at: 11 months ago - Pushed at: about 3 years ago - Stars: 1 - Forks: 0

tum-vision/nnascg

Source code for experiments in paper "Deriving Neural Network Design and Learning from the Probabilistic Framework of Chain Graphs" by Yuesong Shen and Daniel Cremers.

Language: Python - Size: 33.2 KB - Last synced at: about 1 month ago - Pushed at: almost 5 years ago - Stars: 4 - Forks: 1

NewZsh/Notes-for-Learning-Theory

Machine learning theory

Language: TeX - Size: 13.6 MB - Last synced at: almost 2 years ago - Pushed at: over 2 years ago - Stars: 2 - Forks: 0

neuroquant/jf2016-skggm

Tutorials to estimate inverse covariance matrices using the skggm package

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FirstHandScientist/amp

Advanced Message Passing

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jaks19/ensemble_gms

Undirected graphical models are compact representations of joint probability distributions over random variables. To solve inference tasks of interest, graphical models of arbitrary topology can be trained using empirical risk minimization. However, to solve inference tasks that were not seen during training, these models (EGMs) often need to be re-trained. Instead, we propose an inference-agnostic adversarial training framework which produces an infinitely-large ensemble of graphical models (AGMs). The ensemble is optimized to generate data within the GAN framework, and inference is performed using a finite subset of these models. AGMs perform comparably with EGMs on inference tasks that the latter were specifically optimized for. Most importantly, AGMs show significantly better generalization to unseen inference tasks compared to EGMs, as well as deep neural architectures like GibbsNet and VAEAC which allow arbitrary conditioning. Finally, AGMs allow fast data sampling, competitive with Gibbs sampling from EGMs.

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LeviBorodenko/dortmund2array

Tool to convert datasets from "Benchmark Data Sets for Graph Kernels" (K. Kersting et al., 2016) into a format suitable for deep learning research.

Language: Python - Size: 25.4 KB - Last synced at: 25 days ago - Pushed at: over 5 years ago - Stars: 2 - Forks: 0

ArminKaramzade/Social-Matrix-Factorization

Exploiting scoial networks data in recommender systems.

Language: Python - Size: 917 KB - Last synced at: about 2 years ago - Pushed at: almost 5 years ago - Stars: 4 - Forks: 2

MinhDg00/pgm

implementation of some inference and learning algorithms in probabilistic graphical model

Language: Python - Size: 21.5 KB - Last synced at: about 2 years ago - Pushed at: almost 4 years ago - Stars: 0 - Forks: 0

renxiongliu/hdInference

R package on likelihood ratio test for a subset of parameters in Gaussian graphical model

Language: C - Size: 622 KB - Last synced at: over 1 year ago - Pushed at: over 3 years ago - Stars: 0 - Forks: 0

itsrainingdata/sparsebnUtils

Utilities for learning sparse Bayesian networks

Language: R - Size: 409 KB - Last synced at: about 1 month ago - Pushed at: almost 6 years ago - Stars: 2 - Forks: 1

FirstHandScientist/AlphaBP

Alpha Belief Propagation

Language: Python - Size: 1.78 MB - Last synced at: about 2 years ago - Pushed at: almost 5 years ago - Stars: 1 - Forks: 0

Anguscgm/BRUG_BDMCMC

This is the repository for the C++ code of Bayesian Graphical Regression with Birth-Death Markov Process by Yuen et al.

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arkopaldutt/GML_Glauber_Dynamics

Algorithms for Learning Graphical Models from Time-Correlated Samples

Language: Jupyter Notebook - Size: 169 MB - Last synced at: 3 months ago - Pushed at: over 4 years ago - Stars: 0 - Forks: 0

junkyul/gmid-public

Code for A Weighted Mini-Bucket Bound for Solving Influence Diagrams (UAI 2019) and Join-Graph Decomposition Bounds for Influence Diagrams (UAI 2018).

Language: Python - Size: 5.14 MB - Last synced at: about 1 year ago - Pushed at: over 4 years ago - Stars: 0 - Forks: 1

KrystynaGrzesiak/gslope

Sparse Gaussian graphical models with Sorted L-One Penalized Estimation

Language: R - Size: 353 KB - Last synced at: 3 months ago - Pushed at: over 4 years ago - Stars: 3 - Forks: 0

jessewiles/cdots

Curated timelines

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nvihrs14/tcherry

This R-package is for learning the structure of the type of graphical models called t-cherry trees from data. The structure is determined either directly from data or by increasing the order of a lower order t-cherry tree.

Language: R - Size: 301 KB - Last synced at: 1 day ago - Pushed at: almost 5 years ago - Stars: 0 - Forks: 4

javzapata/Presentations

Presentations I have prepared for different courses and lab seminar throughout my PhD

Size: 44.3 MB - Last synced at: about 2 years ago - Pushed at: almost 5 years ago - Stars: 0 - Forks: 0

netw0rkf10w/NoRELAX-cpp

C++ implementation of the NoRELAX methods presented in Continuous Relaxation of MAP Inference: A Nonconvex Perspective (CVPR 2018)

Language: C++ - Size: 2.39 MB - Last synced at: 7 months ago - Pushed at: almost 5 years ago - Stars: 0 - Forks: 0

rizalzaf/drgm

Distributionally Robust Graphical Models

Language: Julia - Size: 19.5 KB - Last synced at: about 2 years ago - Pushed at: over 6 years ago - Stars: 2 - Forks: 0

jnikhilreddy/Hidden-Markov-Models

Implementation of Hidden Markov model

Language: Python - Size: 7.53 MB - Last synced at: about 2 years ago - Pushed at: about 5 years ago - Stars: 0 - Forks: 0

itsayushthada/PGMs

Repository for tasks like Representation, Inference and Learning of Probabilistic Graphical Models.

Language: MATLAB - Size: 4.74 MB - Last synced at: almost 2 years ago - Pushed at: over 5 years ago - Stars: 0 - Forks: 0

nghiapickup/ssl_mincut_graphical_model

Simple graphical model for semi-supervised learning

Language: Python - Size: 16.3 MB - Last synced at: about 2 years ago - Pushed at: almost 3 years ago - Stars: 2 - Forks: 0

m-clark/lords-paradox

Lord's paradox

Language: R - Size: 1.97 MB - Last synced at: about 2 months ago - Pushed at: over 7 years ago - Stars: 2 - Forks: 0

gregschuit/Bayesian-Inference

Probabilistic Machine Learning based on the course IIC3695 - Tópicos Avanzados de Inteligencia de Máquina

Language: Jupyter Notebook - Size: 3.81 MB - Last synced at: about 2 years ago - Pushed at: almost 6 years ago - Stars: 0 - Forks: 0

Shakikhanli/Solar-System

Language: JavaScript - Size: 10.8 MB - Last synced at: about 2 years ago - Pushed at: about 6 years ago - Stars: 0 - Forks: 0

benjaminfrot/lasso-type-estimators

Language: MATLAB - Size: 4.27 MB - Last synced at: about 2 years ago - Pushed at: about 6 years ago - Stars: 0 - Forks: 1

robert-giaquinto/dapper

Fast implementation of DAP topic model using Conjugate Computation Variational Inference

Language: Python - Size: 54.1 MB - Last synced at: 12 days ago - Pushed at: over 6 years ago - Stars: 1 - Forks: 1

zhiyzuo/java-topic-model

Topic modeling algorithm implementation in Java

Language: Java - Size: 1.94 MB - Last synced at: about 2 years ago - Pushed at: over 7 years ago - Stars: 0 - Forks: 1