particle-filtering

BeatNet is state-of-the-art (Real-Time) and Offline joint music beat, downbeat, tempo, and meter tracking system using CRNN and particle filtering. (ISMIR 2021's paper implementation).

Solutions for the Projects of the Artificial Intelligence (CS 188) course of UC Berkeley

Experimental modular probabilistic programming language in Rust, with modeling and inference separated by a generative function trait interface.

Artificial Intelligence + Deep Learning

Source code for reproducing results in several papers related to particle filtering

R Package: Routines for the analysis of passive acoustic telemetry data (Lavender et al., 2023).

A state-space CNV Caller with Disease Profiling

Experiments for online learning and data assimilation for time series data.

Tensorflow framework implementing classical and advanced state-space filtering methods, including Kalman filters, particle filters, particle flow (EDH/LEDH, invertible PF-PF, kernel-embedded), and differentiable particle filtering with entropy-regularized optimal transport (Sinkhorn).

Deep Recurrent Learning for Bayesian State Estimation of Autonomous Six Degree-of-Freedom Optical Lunar Lander

PF-AAE: tracking of 3D object pose via particle filtering (PF) and augmented autoencoders (AAE) -- demo repo

Non-linear state-space model and particle filtering framework for detecting latent liquidity stress and modeling systemic risk transitions in global financial markets.

Methods supporting Lavender, E. et al. (2025). Particle algorithms for animal movement modelling in receiver arrays. Methods in Ecology and Evolution. https://doi.org/10.1111/2041-210X.70028

Localization and tracking with sequential Monte Carlo methods, i.e. particle filtering.

UCL MSc dissertation implementing Bayesian stochastic volatility models for financial time-series forecasting using Sequential Monte Carlo and Hamiltonian Monte Carlo methods.