2 citations · 2 across the 4 of their papers we have counts for
4 papers
Poisson approximate likelihood compared to the particle filter
Yize Hao, Aaron A. Abkemeier, Edward L. Ionides
Filtering algorithms are fundamental for inference on partially observed stochastic dynamic systems, since they provide access to the likelihood function and hence enable likelihoo…
A tutorial on panel data analysis using partially observed Markov processes via the R package panelPomp
Carles Breto, Jesse Wheeler, Aaron A. King +1
The R package panelPomp supports analysis of panel data via a general class of partially observed Markov process models (PanelPOMP). This package tutorial describes how the mathema…
Accelerated Inference for Partially Observed Markov Processes using Automatic Differentiation
Kevin Tan, Giles Hooker, Edward L. Ionides
Automatic differentiation (AD) has driven recent advances in machine learning, including deep neural networks and Hamiltonian Markov Chain Monte Carlo methods. Partially observed n…
Monte Carlo profile confidence intervals
Edward L. Ionides, Carles Breto, Joonha Park +2
Monte Carlo methods to evaluate and maximize the likelihood function enable the construction of confidence intervals and hypothesis tests, facilitating scientific investigation usi…