The basic distribution probability Tutorial for Deep Learning Researchers
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Updated
Oct 1, 2020 - Python
The basic distribution probability Tutorial for Deep Learning Researchers
Pytorch implementations of density estimation algorithms: BNAF, Glow, MAF, RealNVP, planar flows
Generate realizations of stochastic processes in python.
Normalizing flows in PyTorch
Anomaly detection using LoOP: Local Outlier Probabilities, a local density based outlier detection method providing an outlier score in the range of [0,1].
Machine Learning with Symbolic Tensors
📦 Python library for Stochastic Processes Simulation and Visualisation
UQpy (Uncertainty Quantification with python) is a general purpose Python toolbox for modeling uncertainty in physical and mathematical systems.
📃Language Model based sentences scoring library
Generative Probabilistic Novelty Detection with Adversarial Autoencoders
Likelihood-free AMortized Posterior Estimation with PyTorch
Markov Chains and Hidden Markov Models in Python
Autonomous Mobile Robot developed and programmed in the online course named "Self-Driving and ROS 2 - Learn By Doing! Odometry & Control"
Probabilistic data structures in python http://pyprobables.readthedocs.io/en/latest/index.html
Probabilistic Programming and Statistical Inference in PyTorch
A library for discrete-time Markov chains analysis.
A Home Assistant integration to accurately and intelligently track occupancy of an area
Completion After Prompt Probability. Make your LLM make a choice
Bayesian A/B testing
Командный репозиторий.
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