Latin hypercube sampler with sudoku constraint
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Updated
Apr 26, 2017 - Python
Latin hypercube sampler with sudoku constraint
Visualization of different distribution sampling methods.
Collection of Monte Carlo (MC) and Markov Chain Monte Carlo (MCMC) algorithms applied on simple examples.
Methods for generating directed simple graphs with a prescribed degree sequence
Some methods to sampling data points from a given distribution.
Implementing inference methods for Latent Dirichlet Allocation model. This repo is for study purposes.
A basic binary classification class that uses sampling techniques in order to deal with rare events (e.g. 10% or less).
Thermostat-assisted continuously-tempered Hamiltonian Monte Carlo for Bayesian learning
PyTorch implementation for " Differentiable Antithetic Sampling for Variance Reduction in Stochastic Variational Inference" (https://arxiv.org/abs/1810.02555).
Implementation of the Mineral algorithm as described in the paper, Mineral: Multi-modal Network Representation Learning.
A simple utility to perform sampling from multivariate distributions (supported by a PyTorch backend)
Experimental code: adaptive importance sampling for bayesian networks.
This repo contain ressources of the course EEJSI
Final project for IEE 520 Stat learning for data mining. Highly imbalanced data set. Sampling methods used.
Implementation of active learning sampler
Python tools to sample randomly with dont pick closest n elements constraints. Also contains a batch generator for the same to sample with replacement and with repeats if necessary.
Sampling and resampling techniques for random sample generation, estimation, and simulation
Tools for efficient global sensitivity analyses for models with correlated input parameters.
A small python module to play with the "friendship paradox" (graph sampling bias).
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