There are 7 repositories under monte-carlo-methods topic.
Collection of notebooks about quantitative finance, with interactive python code.
Repository for most of the code from my YouTube channel
A blog which talks about machine learning, deep learning algorithms and the Math. and Machine learning algorithms written from scratch.
State estimation, smoothing and parameter estimation using Kalman and particle filters.
Code repository for my course on the fundamentals of reinforcement learning
Deep active inference agents using Monte-Carlo methods
This repository contains implementations of some basic sampling methods in numpy.
Author's implementation of SIGGRAPH 2023 paper, "A Practical Walk-on-Boundary Method for Boundary Value Problems"
Reinforcement Learning Short Course
Reinforcement Learning - Implementation of Exercises, algorithms from the book Sutton Barto and David silver's RL course in Python, OpenAI Gym.
Python code of commonly used stochastic models for Monte-Carlo simulations
Implementation of Hierarchical Control for Head-to-Head Autonomous Racing paper
Implementation of fundamental concepts and algorithms for reinforcement learning
This repository includes Matlab codes/routines that were used in our manuscript entitled "Importance sampling for a robust and efficient multilevel Monte Carlo estimator for stochastic reaction networks" that can be found in this preprint: https://arxiv.org/abs/1911.06286
Tools for Stochastic Simulation using diffusion models (R).
Approximate Bayesian Computation (ABC) with differential evolution (de) moves and model evidence (Z) estimates.
This repository has RL algorithms implemented using python
Uses Monte Carlo Methods to determine the probability a starting hand in poker will win the round.
ISING_2D_SIMULATION is a FORTRAN77 program which carries out a Monte Carlo simulation of a 2D Ising model, using gnuplot to display the initial and final configurations.
A Quantitative Finance Engineering Project
My Python learning experience 📚🖥📳📴💻🖱✏
Numerical Simulation Laboratory at Unimi in 2020-2021 (D.E. Galli). Advanced Monte Carlo methods: Markov chains, Metropolis algorithm. Numerical simulations in statistical mechanics. Stochastic calculus and stochastic differential equation. Computational intelligence, stochastic optimization. Parallel computing and parallel programming. Machine learning and deep neural networks
Solutions for course: "Applied Game Theory" taken at University of Novi Sad - Faculty of Technical Sciences
Program that estimates the Gini coefficient of a country using Lagrange Interpolation for Lorenz curve approximation.
Hedging options by using Monte Carlo simulations or real data
solving a simple 4*4 Gridworld almost similar to openAI gym frozenlake using Monte-Carlo method Reinforcement Learning
A reinforcement learning framework for the game of Nim.