Displaying 1 to 20 from 22 results

Stock-Prediction-Models - Gathers machine learning and deep learning models for Stock forecasting including trading bots and simulations

  •    Jupyter

Stock-Prediction-Models, Gathers machine learning and deep learning models for Stock forecasting, included trading bots and simulations. I code LSTM Recurrent Neural Network and Simple signal rolling agent inside Tensorflow JS, you can try it here, huseinhouse.com/stock-forecasting-js, you can download any historical CSV and upload dynamically.

Virtual Photonics

  •    CSharp

The Virtual Photonics Technology Initiative was established by the Laser Microbeam and Medical Program (LAMMP), an NIH/NIBIB Biotechnology Resource Center at the Beckman Laser Institute and Medical Clinic (http://lammp.bli.uci.edu/) in April 2008.

McCloud Service Framework

  •    

Monte Carlo Cloud Service Framework (McCloud) provides a generic service implementation of Monte Carlo method, based on Microsoft Windows Azure, to solve a wide range of scientific and engineering problems.

Mindless Setback

  •    CSharp

Setback is a card game popular in New England. This project uses a combination of brute force and Monte Carlo methods to play Setback. This is an experimental approach to playing cards and other games where incomplete information is available.




mersenne-twister - This Mersenne Twister is a fast pseudo-random number generator (PRNG) in C++

  •    C++

This is an implementation of the fast pseudo-random number generator (PRNG) MT19937, colloquially called the Mersenne Twister. It was given this name because it has a period of 2^19937 - 1, which is a Mersenne prime. The code here is actually ~1.6 times faster (on Intel CPUs) than the reference implementation mt19937ar.c (see below).

BDA_R_demos - Bayesian Data Analysis demos for R

  •    HTML

This repository contains some R demos and additional notes for the book Bayesian Data Analysis, 3rd ed by Gelman, Carlin, Stern, Dunson, Vehtari, and Rubin (BDA3). Currently there are demos for BDA3 Chapters 2, 3, 4, 5, 6, 10 and 11. Furthermore there are demos for RStan and RStanARM. There are also reading instructions and additional notes for chapters 1-8, 10-11.

dynesty - Dynamic Nested Sampling package for computing Bayesian posteriors and evidences

  •    Python

A Dynamic Nested Sampling package for computing Bayesian posteriors and evidences. Pure Python. MIT license. Beta release. Documentation can be found here. Warning: The documentation is currently somewhat out of date.


monte-carlo - Python Example of Monte Carlo Estimation & Importance Sampling

  •    Jupyter

Python Example of Monte Carlo Estimation & Importance Sampling

bayesian-scribbles - This repository contains code that I scribble while learning Bayesian Inference

  •    Jupyter

This repository contains code that I scribble while learning PGMs & Bayesian Inference. This will roughly follow Sir David MacKay's lectures on Information Theory, Pattern Recognition, and Neural Networks. Another great resource to read about some of the stuff in this repository is Stefano Ermon's notes.

skpr - Generates and evaluates D, I, A, Alias, E, T, G, and custom optimal designs

  •    R

Generates and evaluates D, I, A, Alias, E, T, G, and custom optimal designs. Supports generation and evaluation of mixture and split/split-split/N-split plot designs. Includes parametric and Monte Carlo power evaluation functions. Provides a framework to evaluate power using functions provided in other packages or written by the user.

thejoker - A custom Monte Carlo sampler for the (gravitational) two-body problem

  •    Python

A custom Monte Carlo sampler for the two-body problem. See the documentation for information on how to install and use The Joker.

mcts - Domain independent implementation of Monte Carlo Tree Search methods

  •    Java

This is a Java implementation of Monte Carlo Tree Search methods. It is self-contained, domain-independent and can thus easily be used in any state-action domain. The project was developed for the purpose of my Bachelor’s thesis. Create the implementation of MctsDomainAgent.

c-ray - C-Ray is a small, simple path tracer written in C

  •    C

C-ray is a simple path tracer built for studying computer graphics. It's also a great platform for developing your own raytracing algorithms. Just write your own rayTrace() function! Multithreading, 3D model loading and render previews are handled by C-ray, so you can concentrate on the core principles.

StarryNight - Monte Carlo codes to simulate polar domains in hybrid perovskite solar cells

  •    C

Monte Carlo codes to simulate dipole-dipole interactions and ferroelectric domains in a hybrid organic-inorganic perovskite solar cell. A C compiler such as gcc is required for the main code, while various scripts and post-processing tools use a combination of python, julia, and gnuplot.

Bioindustrial-Park - BioSTEAM's Premier Repository for Biorefinery Models and Results

  •    Jupyter

The Bioindustrial-Park is the premier repository for complete biorefinery models and results generated with BioSTEAM. The repository is meant to foster accessibility and deeper communication within the BioSTEAM biorefinery simulation community. All biorefinery models are in the "BioSTEAM {version}/biorefineries" package, and the excel results for each biorefinery are in the "biorefinery_results" folder. See LICENSE.txt for information on the terms & conditions for usage of this software, and a DISCLAIMER OF ALL WARRANTIES.

biosteam - The Biorefinery Simulation and Techno-Economic Analysis Modules; Chemical Process Simulation Under Uncertainty

  •    Python

BioSTEAM is a fast and flexible package for the design, simulation, and techno-economic analysis of biorefineries under uncertainty [1]. BioSTEAM is built to streamline and automate early-stage technology evaluations and to enable rigorous sensitivity and uncertainty analyses. Complete biorefinery configurations are available at the Bioindustrial-Park GitHub repository, BioSTEAM's premier repository for biorefinery models and results. The long-term growth and maintenance of BioSTEAM is supported through both community-led development and the research institutions invested in BioSTEAM, including the Center for Advanced Bioenergy and Bioproducts Innovation (CABBI). Through its open-source and community-lead platform, BioSTEAM aims to foster communication and transparency within the biorefinery research community for an integrated effort to expedite the evaluation of candidate biofuels and bioproducts. All data on chemicals, fitted coefficients, and functions to estimate chemical properties are imported from chemicals, a community-driven open-source library developed by Caleb Bell. The thermosteam library, BioSTEAM's premire thermodynamic engine, creates thermodynamic property packages by implementing both the mixing rules and the algorithms for thermodynamic equilibrium and by relying on the chemicals library for pure component models.

paramonte - ParaMonte: Plain Powerful Parallel Monte Carlo and MCMC Library for Python, MATLAB, Fortran, C++, C

  •    Fortran

ParaMonte is a serial/parallel library of Monte Carlo routines for sampling mathematical objective functions of arbitrary-dimensions, in particular, the posterior distributions of Bayesian models in data science, Machine Learning, and scientific inference, with the design goal of unifying the automation (of Monte Carlo simulations), user-friendliness (of the library), accessibility (from multiple programming environments), high-performance (at runtime), and scalability (across many parallel processors). Full automation of all Monte Carlo simulations to the highest levels possible to ensure the highest level of user-friendliness of the library and minimal time investment requirements for building, running, and post-processing of simulation models.

lca_algebraic - Layer over brightway2 for algebraic definition of parametric models and super fast computation of LCA

  •    Jupyter

This library is a small layer above brightway2, designed for the definition of parametric inventories with fast computation of LCA impacts, suitable for monte-carlo analyis. Alternatively, you can download and execute this installer. It will setup a full anaconda environment with Jupyter, Brightway2 and LCA Algebraic.






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