bayesian inference 1maximum likelihood estimation 1non-gaussian filter 1particle filter comparison 1state-space models 1
From the 1 of 3 linked papers with an AI index.
3 papers
stat.CO2026
Maximum Likelihood and Bayesian Estimation for State-Space Models Using the Non-Gaussian Filter
Genshiro Kitagawa
The paper revisits deterministic non‑Gaussian filtering for nonlinear state‑space models, showing it can be used effectively for maximum likelihood and Bayesian estimation thanks t…
stat.ME2026
Backward Smoothing versus Fixed-Lag Smoothing in Particle Filters
Genshiro Kitagawa
Particle smoothing enables state estimation in nonlinear and non-Gaussian state-space models, but its practical use is often limited by high computational cost. Backward smoothing…
stat.ME2026
Bayesian Optimization of Noisy Log-Likelihoods Evaluated by Particle Filters -- One Parameter Case --
Genshiro Kitagawa
Likelihood functions evaluated using particle filters are typically noisy, computationally expensive, and non-differentiable due to Monte Carlo variability. These characteristics m…