Proposes adaptive multiple importance sampling for robust Bayesian model evidence estimation under parameter non-identifiability, shown to outperform deterministic methods on ecological case studies while being cheaper than MCMC.
The Annals of Statistics , author=
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A betweenness centrality for stochastic networks is defined via an absorbing Markov chain on sequences of reported central nodes, with importance given by pre-absorption occupancy and estimated by Monte Carlo.
A functional central limit theorem for pattern frequencies in 2D samples enables nonparametric goodness-of-fit, two-sample, and symmetry tests for copulas, with bootstrap critical values and parametric examples.
crossfit is an R package that supplies a general-purpose cross-fitting engine driven by user-specified DAGs of nuisance models with configurable fold allocations and reproducibility features.
Boson correlations for states with well-behaved Glauber-Sudarshan P-representations are spurious statistical correlations due to Simpson's paradox from symmetry-breaking in ensemble averages over varying geometries.
citing papers explorer
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Reliable model selection in the presence of parameter non-identifiability
Proposes adaptive multiple importance sampling for robust Bayesian model evidence estimation under parameter non-identifiability, shown to outperform deterministic methods on ecological case studies while being cheaper than MCMC.
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Betweenness Central Nodes Under Uncertainty: An Absorbing Markov Chain Approach
A betweenness centrality for stochastic networks is defined via an absorbing Markov chain on sequences of reported central nodes, with importance given by pre-absorption occupancy and estimated by Monte Carlo.
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Pattern-based tests for two-dimensional copulas
A functional central limit theorem for pattern frequencies in 2D samples enables nonparametric goodness-of-fit, two-sample, and symmetry tests for copulas, with bootstrap critical values and parametric examples.
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crossfit: A Graph-Based Cross-Fitting Engine in R
crossfit is an R package that supplies a general-purpose cross-fitting engine driven by user-specified DAGs of nuisance models with configurable fold allocations and reproducibility features.
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Boson correlations are spurious for classical states
Boson correlations for states with well-behaved Glauber-Sudarshan P-representations are spurious statistical correlations due to Simpson's paradox from symmetry-breaking in ensemble averages over varying geometries.