320 citations · 760 across the 27 of their papers we have counts for
6 papers · 1 filter
panelPomp: Analysis of Panel Data via Partially Observed Markov Processes in R
Carles Bretó, Jesse Wheeler, Aaron A. King +1
Panel data arise when time series measurements are collected from multiple, dynamically independent but structurally related systems. Each system's time series can be modeled as a…
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…
Income, health, and spurious cointegration
José A. Tapia Granados, Edward L. Ionides
Data for many nations show a long-run increase, over many decades, of income, indexed by GDP per capita, and population health, indexed by mortality or life expectancy at birth (LE…
Exact phylodynamic likelihood via structured Markov genealogy processes
Aaron A. King, Qianying Lin, Edward L. Ionides
We show that each member of a broad class of Markovian population models induces a unique stochastic process on the space of genealogies. We construct this genealogy process and de…