Accelerating pseudo-marginal Metropolis-Hastings by correlating auxiliary variables
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Updated
Nov 29, 2017 - Python
Accelerating pseudo-marginal Metropolis-Hastings by correlating auxiliary variables
Accelerating Monte Carlo methods for Bayesian inference in dynamical models
Using a 2-dimensional Particle Filter to localize a vehicle
Correlated pseudo-marginal Metropolis-Hastings using quasi-Newton proposals
Particle Filter estimators using C++ Multibody Dinamics library Simbody
Kidnapped Vehicle (project 6 of 9 from Udacity Self-Driving Car Engineer Nanodegree)
Localization in a static map, planning in a local map.
Android applications for SmartPhoneSensing course for indoor localization
Bayesian Particle Learning models in R
SLAM navigation on simplified scenario (FastSLAM implementation using Python) based on Particle Filter (Sequential Monte Carlo). What happens when the visual support of a drone is missing?
Hybrid Extended Kalman Filter and Particle Filter. Graded project for the ETH course "Recursive Estimation".
Simultaneous State Estimation and Dynamics Learning from Indirect Observations.
This project implements grid-based FastSLAM1.0 and FastSLAM2.0 algorithms to solve SLAM problem in a simulated environment.
HILO-MPC is a Python toolbox for easy, flexible and fast development of machine-learning-supported optimal control and estimation problems
Experiments for online learning and data assimilation for time series data.
State estimation and filtering algorithms in Go
A data assimilation experiment with the DALEC ecosystem model
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