320 citations · 760 across the 27 of their papers we have counts for
14 papers · 1 filter
Iterating marginalized Bayes maps for likelihood maximization with application to nonlinear panel models
Jesse Wheeler, Aaron J. Abkemeier, Edward L. Ionides
Complex dynamic systems can be investigated by fitting mechanistic stochastic dynamic models to time series data. In this context, commonly used Monte Carlo inference procedures fo…
Poisson Approximate Likelihood versus the block particle filter for a spatiotemporal measles model
Kunyang He, Yize Hao, Edward L. Ionides
Filtering algorithms for high-dimensional nonlinear non-Gaussian partially observed stochastic processes provide access to the likelihood function and hence enable likelihood-based…
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…
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…
Revisiting Inference for ARMA Models: Improved Fits and Superior Confidence Intervals
Jesse Wheeler, Edward L. Ionides
Autoregressive moving average (ARMA) models are widely used for analyzing time series data. However, standard likelihood-based inference methodology for ARMA models has avoidable l…