derivative-free NN optimiser using MCMC algorithms
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Updated
May 28, 2020 - Jupyter Notebook
derivative-free NN optimiser using MCMC algorithms
The code interface is written in R, and for the sake of speed, most parts are written in C++. However, no prerequisite knowledge for both languages is required to run the code. An R file called runInfHMM.R sources all needed functions to compile and run the code.
Using a Bayesian Hierarchical Model to analyze North Carolina voter registration data
Blang core (parsing, generation, eclipse plug-in)
We propose a particle MCMC sampler to learn the kinetic parameters of a chemical system, specifically the adsorption and desorption of CO on Pd(111).
Dual Network Hawkes Process -- Analyzing Topic Transitions in Text-Based Social Cascades
Using an advanced Markov Chain Monte Carlo sampling algorithm, this agent is capable of decrypting encrypted texts.
Stochastic Approximation Monte Carlo (SAMC) Sampler and Methods
Parallel MCMC method for graph coloring
Bayesian inference tools. Including state-of-the-art inference methods: HMC family, ABC family, Data assimilation, and so on. Part of Mathepia.jl
Assignments Solution for Foundations of Machine Learning Course
Instructed by : Prof. Manisha Pal. A repository created with the practical problems on Bayesian computing and some advance computing related to MCMC, Metropolis etc.
Data Science Blog
Sandbox repo for Bayesian statistics and modeling
A comparison of basic models written in pystan vs pymc3
Samplers from the paper "Stochastic Gradient MCMC with Repulsive Forces"
Sampling from probability distribution function using the Metropolis-Hastings algorithm
Parametric Bayesian Instrumental Variable Methods.
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