A survey of Monte Carlo methods for parameter estimation
Statistical signal processing applications usually require the estimation of some parameters
of interest given a set of observed data. These estimates are typically obtained either by …
of interest given a set of observed data. These estimates are typically obtained either by …
A review of modern computational algorithms for Bayesian optimal design
Bayesian experimental design is a fast growing area of research with many real‐world
applications. As computational power has increased over the years, so has the development …
applications. As computational power has increased over the years, so has the development …
Optimal experimental design: Formulations and computations
Questions of 'how best to acquire data'are essential to modelling and prediction in the
natural and social sciences, engineering applications, and beyond. Optimal experimental …
natural and social sciences, engineering applications, and beyond. Optimal experimental …
Structural damage localization and quantification based on a CEEMDAN Hilbert transform neural network approach: a model steel truss bridge case study
Vibrations of complex structures such as bridges mostly present nonlinear and non-
stationary behaviors. Recently, one of the most common techniques to analyze the nonlinear …
stationary behaviors. Recently, one of the most common techniques to analyze the nonlinear …
Deep adaptive design: Amortizing sequential bayesian experimental design
Abstract We introduce Deep Adaptive Design (DAD), a method for amortizing the cost of
adaptive Bayesian experimental design that allows experiments to be run in real-time …
adaptive Bayesian experimental design that allows experiments to be run in real-time …
Optimizing sequential experimental design with deep reinforcement learning
Bayesian approaches developed to solve the optimal design of sequential experiments are
mathematically elegant but computationally challenging. Recently, techniques using …
mathematically elegant but computationally challenging. Recently, techniques using …
A survey of recent advances in particle filters and remaining challenges for multitarget tracking
X Wang, T Li, S Sun, JM Corchado - Sensors, 2017 - mdpi.com
We review some advances of the particle filtering (PF) algorithm that have been achieved in
the last decade in the context of target tracking, with regard to either a single target or …
the last decade in the context of target tracking, with regard to either a single target or …
An invitation to sequential Monte Carlo samplers
ABSTRACT Statisticians often use Monte Carlo methods to approximate probability
distributions, primarily with Markov chain Monte Carlo and importance sampling. Sequential …
distributions, primarily with Markov chain Monte Carlo and importance sampling. Sequential …
Bayesian sequential optimal experimental design for nonlinear models using policy gradient reinforcement learning
We present a mathematical framework and computational methods for optimally designing a
finite sequence of experiments. This sequential optimal experimental design (sOED) …
finite sequence of experiments. This sequential optimal experimental design (sOED) …
A review of efficient applications of genetic algorithms to improve particle filtering optimization problems
C Kuptametee, ZH Michalopoulou, N Aunsri - Measurement, 2024 - Elsevier
Particle filtering (PF) is a sequential Monte Carlo method that draws sample (particle) values
of state variables of interest to approximate the posterior probability distribution function …
of state variables of interest to approximate the posterior probability distribution function …