probabilistic

Unsupervised Learning for Image Registration

A Python package for Bayesian forecasting with object-oriented design and probabilistic models under the hood.

PyTorch based Probabilistic Time Series forecasting framework based on GluonTS backend

Bloom filter for Scala, the fastest for JVM

《概率机器人》书和课后习题

ProbLog is a Probabilistic Logic Programming Language for logic programs with probabilities.

JS implementation of probabilistic data structures: Bloom Filter (and its derived), HyperLogLog, Count-Min Sketch, Top-K and MinHash

Quickly detect already witnessed data.

UQpy (Uncertainty Quantification with python) is a general purpose Python toolbox for modeling uncertainty in physical and mathematical systems.

Depth-Based Bayesian Object Tracking Library

Density Adaptive Point Set Registration

Counter Data structure for Golang using CountMin Sketch with a fixed amount of memory

An in-memory bloom filter with persistence and HTTP interface

🎲 Efficient Java implementation of the probabilistic Earley algorithm to parse Stochastic Context Free Grammars (SCFGs)

Count-Min Sketch Implementation in C

Probabilistic Dialogue Act Classification for the Switchboard Corpus using an LSTM model

Probabilistic plane extraction

JudgeD: Probabilistic Datalog with Dependencies

A fairly robust LeetSpeak to English converter using probability of word usage

Bloom filter implementation

🥄✨Time-series Benchmark methods that are Simple and Probabilistic

PROVIDE: A Probabilistic Framework for Unsupervised Video Decomposition (UAI 2021)

Probabilistic data structures java implementation.

A pure, simple and fast pythonic bloom filter

Scala embedded universal probabilistic programming language

This algorithm is a probabilistic approach for approximating the global optimum for the Simple Assembly Line Balancing Problem.

A novel, space-efficient, probabilistic data structure for hashing a large number of values. It is similar to a hash table, but smaller because it does not need to store the entire value like HashTable and has better asymptotic performance, at the cost of possible false-positives, a likelihood that is configurable. False negatives are not possible. Best use case: Lightweight check if a value/query exists to reject needless/bogus call in place before performing a more latent or expensive request, such as a search for content or database call.

HyperLogLog implementations.

Official PyTorch implementation for "PROPEL: Probabilistic Parametric Regression Loss for Convolutional Neural Networks"