Scott, Dagim Shiferaw Tadele, Otso Ovaskainen Statistically analysing interacting particle systems remains challenging because the governing equations are analytically intractable. Existing solutions include moment closure methods with pseudolikelihood-based frameworks, and likelihood-free frameworks based on extensive simulations, both relying on heuristic choices whose validity is difficult to predict.
Scott, Dagim Shiferaw Tadele, Otso Ovaskainen Statistically analysing interacting particle systems remains challenging because the governing equations are analytically intractable.
As a resolution, we rigorously derive an asymptotically exact expression for the likelihood of agent-based models (ABMs) operating in continuous space and time that can be formulated as...
Existing solutions include moment closure methods with pseudolikelihood-based frameworks, and likelihood-free frameworks based on extensive simulations, both relying on heuristic choices whose validity is...
The page is ready to read now. The fuller skim-friendly version will appear here automatically.
Scott, Dagim Shiferaw Tadele, Otso Ovaskainen Statistically analysing interacting particle systems remains challenging because the governing equations are analytically intractable. Existing solutions include moment closure methods with pseudolikelihood-based frameworks, and likelihood-free frameworks based on extensive simulations, both relying on heuristic choices whose validity is difficult to predict. As a resolution, we rigorously derive an asymptotically exact expression for the likelihood of agent-based models (ABMs) operating in continuous space and time that can be formulated as reactant–catalyst–product (RCP) models.
by Niklas Moser, Dmitri Finkelshtein, Georgy Chargaziya, Stephen J.
Open the app view to save this story, compare related coverage, and continue from the same source.