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Implementation of Markov chain Monte Carlo sampling and the Metropolis-Hastings algorithm for multi-parameter Bayesian inference.

License: MIT License

C++ 46.92% Python 53.08%
bayesian-inference bayesian-statistics c-plus-plus markov-chain-monte-carlo mcmc-sampling metropolis-hastings posterior-probability python

bayesianinference's Introduction

Repositories (from scratch) about data structures, fuzzy logic, machine learning, metaheuristic optimization, and robotics.

C++

ANFIS-metaheuristic - Multivariate regression and classification using an adaptive neuro-fuzzy inference system (Takagi-Sugeno) and metaheuristic optimization.

BayesianInference - Implementation of Markov chain Monte Carlo sampling and the Metropolis-Hastings algorithm for multi-parameter Bayesian inference.

SimulatedAnnealing - Implementation of metaheuristic optimization using population-based simulated annealing.

Python

ANFIS - Multivariate regression and classification using an adaptive neuro-fuzzy inference system (Takagi-Sugeno) and particle swarm optimization.

BayesianInference - Implementation of Markov chain Monte Carlo sampling and the Metropolis-Hastings algorithm for multi-parameter Bayesian inference.

BinaryTree - Binary tree data structure using a binary node data structure.

Clustering - Implementation of K-means and fuzzy C-means clustering methods using a naive algorithm and particle swarm optimization.

DataStructures - Basic data structures (stack, queue, priority queue, binary heap.)

DecisionTree - Regression using decision tree, random tree, bootstrap aggregating (bagging), and boosting.

FeedForwardNN - Multivariate regression and classification using a feed-forward neural network and gradient descent optimization.

GridSearch - Two-dimensional grid search using depth first search, breath first search, A* algorithm, and Dijkstra’s algorithm.

HashTable - Hash table and dictionary class implementation using lists and double-linked lists.

LinkedLists - Single and double linked list data structures.

PathPlanning - Implementation of particle swarm optimization (PSO) for path planning when the environment is known.

PSO - Metaheuristic minimization using particle swarm optimization.

Q-Learning - Reinforcement learning using Q-learning, double Q-learning, and Dyna-Q.

RegressionGDO - Multivariate linear and logistic regression using gradient descent optimization.

SignalFilters - Signal filtering and generation of synthetic time-series.

SimulatedAnnealing - Implementation of metaheuristic optimization using population-based simulated annealing.

Sorting - Implementation of sorting and searching functions for lists and arrays.

SpaceDyn - A toolbox for space, mobile, and humanoid robotic systems (work in progress).

Matlab

ClassificationNN - Multivariate classification using a feed-forward neural network and backpropagation.

FingerControl - Biomimetic Control of an Artificial Finger for Rehabilitation Robotics Using Shape Memory Alloy Actuators.

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