renhongjia,rhongjia,github

pymc

PyMC: Bayesian Stochastic Modelling in Python (DEPRECATED: for PyMC3: https://github.com/pymc-devs/pymc3)

pymc3

Probabilistic Programming in Python: Bayesian Modeling and Probabilistic Machine Learning with Theano

pyopenpnl

Python Open Probabilistic Networks Library Bindings - Simple PGM & Bayes Net Tools for Python

pyprobml

Python code for "Machine learning: a probabilistic perspective" (2nd edition)

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risknetwork

Risk Network Modeling and Analysis

sequence-classification

skdb

Selective KDB C++ code (JMLR)

slime-mould-algorithm-a-new-method-for-stochastic-optimization-

In this paper, a new stochastic optimizer, which is called slime mould algorithm (SMA), is proposed based upon the oscillation mode of slime mould in nature. The proposed SMA has several new features with a unique mathematical model that uses adaptive weights to simulate the process of producing positive and negative feedback of the propagation wave of slime mould based on bio-oscillator to form the optimal path for connecting food with excellent exploratory ability and exploitation propensity. The proposed SMA is compared with up-to-date metaheuristics in an extensive set of benchmarks to verify the efficiency. Moreover, four classical engineering structure problems are utilized to estimate the efficacy of the algorithm in optimizing engineering problems. The results demonstrate that the algorithm proposed benefits from competitive, often outstanding performance on different search landscapes. The source codes and info of SMA are publicly available at: http://www.alimirjalili.com/SMA.html

snbvbs

Code for "Scalable Bayesian Variable Selection Regression Models for Count Data", by Miao et al. (2019), in Flexible Bayesian Regression Modelling, Yanan F. et al (Eds), Elsevier, 187-219.

standard-and-chaotic-firefly-algorithm

MATLAB implementations of standard and chaos-incorporated versions of the firefly metaheuristic tested on continuous optimization functions.

swarmlib

This repository implements several swarm optimization algorithms and visualizes them. Implemented algorithms: Particle Swarm Optimization (PSO), Firefly Algorithm (FA), Cuckoo Search (CS), Ant Colony Optimization (ACO), Artificial Bee Colony (ABC) and Grey Wolf Optimizer (GWO)