Fast, flexible and easy to use probabilistic modelling in Python.
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Updated
Mar 6, 2025 - Python
Fast, flexible and easy to use probabilistic modelling in Python.
Variational autoencoder implemented in tensorflow and pytorch (including inverse autoregressive flow)
🌲 Stanford CS 228 - Probabilistic Graphical Models
PyHGF: A neural network library for predictive coding
scAR (single-cell Ambient Remover) is a deep learning model for removal of the ambient signals in droplet-based single cell omics
A Library for Modelling Probabilistic Hierarchical Graphical Models in PyTorch
Official Repository of "Contextual Graph Markov Model" (ICML 2018 - JMLR 2020)
MvMM-RegNet: A new image registration framework based on multivariate mixture model and neural network estimation (MICCAI 2020)
Orgainzed Digital Intelligent Network (O.D.I.N)
PyTorch implementation for multivariate mixture model on cardiac segmentation from multi-source images (TPAMI 2019)
A Python Library for Probabilistic Sparse Coding with Non-Standard Priors and Superpositions
A collection of commonly used datasets as benchmarks for density estimation in MaLe
Bayesian nonparametric models for python
A python package for finding causal functional connectivity from neural time series observations.
A Tensorflow implementation of the paper https://arxiv.org/pdf/1803.07710.pdf
[ICML 2024] Probabilistic Conceptual Explainers (PACE): Trustworthy Conceptual Explanations for Vision Foundation Models
⚗️ A curated list of Books, Research Papers, and Software for Bayesian Networks.
Checking D-separations and I-equivalence in Bayesian Networks.
Implementation of the Paper "Entity Linking in Web Tables with Multiple Linked Knowledge Bases"
X-Metric: An N-Dimensional Information-Theoretic Framework for Groupwise Registration and Deep Combined Computing (TPAMI 2023)
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