Little Ball of Fur - A graph sampling extension library for NetworKit and NetworkX (CIKM 2020)
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Updated
Feb 6, 2024 - Python
Little Ball of Fur - A graph sampling extension library for NetworKit and NetworkX (CIKM 2020)
Metropolis-Hastings GAN in Tensorflow for enhanced generator sampling
Some methods to sampling data points from a given distribution.
Applications of distribution modeling and MCMC methods to intention forecasting
Graph: Representation, Learning, and Inference Methods
Python development to solve the 0/1 Knapsack Problem using Markov Chain Monte Carlo techniques, dynamic programming and greedy algorithm.
Accelerating pseudo-marginal Metropolis-Hastings by correlating auxiliary variables
A lightweight Markov Chain Monte Carlo package with focus on Metropolis-Hastings.
Using MCMC to estimate notes in audio, and comparing to human estimation of chords
Monte is a set of Monte Carlo methods in Python. The package is written to be flexible, clear to understand and encompass variety of Monte Carlo methods.
Classical predictive models implemented in Python.
Metropolis Light Transport (Reading Group)
Implementation of Markov chain Monte Carlo sampling and the Metropolis-Hastings algorithm for multi-parameter Bayesian inference.
Constructing Metropolis-Hastings proposals using damped BFGS updates
Code, logs, and final models for SIGSPATIAL SpatialEpi '22: Spatiotemporal Disease Case Prediction using Contrastive Predictive Coding.
Probabilistic Models of Human and Machine Intelligence
Correlated pseudo-marginal Metropolis-Hastings using quasi-Newton proposals
Markov Chain Monte Carlo methods.
Bayesian inference and model selection, Kalman and particle filters, Gibbs sampling, rejection sampling, Metropolis-Hastings
Naming game among GMM agents using the Metropolis-Hastings algorithm. Inter-GMM
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