Some notebooks for learning about bayesian models
-
Updated
Apr 24, 2020 - Jupyter Notebook
Some notebooks for learning about bayesian models
Selected topics in Computational Physics.
Quantitative Finance, Financial Machine Learning and visualizations Notebooks
Financial Mathmatics Concepts with theory & visualizations
A collection of Statistics and ML notebooks useful for Graduate Students
Notebooks for my youtube Reinforcement Learning leactures.
a collection of python notebooks using RL agents to play Atari games in OpenAI gym environments
Jupyter notebooks implementing Reinforcement Learning algorithms in Numpy and Tensorflow
Codes, notebooks and data for the computation of the self-energy and vertex renormalization spinfoam amplitudes. The algorithm is based on Monte Carlo simulations.
Some interesting applications of Stochastic Processes using Jupyter Notebooks for descriptive and instructive illustrations.
Github repo for the submission of the codes and notebooks for the LSN course at UNIMI
Notebook for implementing Monte Carlo techniques (Metropolis-Hastings and Augmented Gibbs) to solve a Bayesian Probit regression.
Codes and notebooks for the application of Markov Chain Monte Carlo in spinfoams. Computation of boundary observables, correlation functions and entanglement entropy.
Code and notebooks for the computation of the 16-cell spinfoam amplitude, including boundary observables and quantum correlations. The computation is based on MCMC .
An approximation of π calculated via Monte Carlo method and proposed in Jupyter Notebook. A solution for Computer Simulation (40634-1 Sharif UT, Spring 2023) homework, the 1st series.
Reinforcement Learning Notebooks
a collection of numerical experiments documented in jupyter notebooks.
This notebook shows how to use variance constrained semi grand canonical (VC-SGC) Molecular Dynamics/Monte Carlo (MD/MC) calculations in pyiron
Implementation notebooks and scripts of Deep Reinforcement learning Algorithms in PyTorch and TensorFlow.
Add a description, image, and links to the monte-carlo topic page so that developers can more easily learn about it.
To associate your repository with the monte-carlo topic, visit your repo's landing page and select "manage topics."