Preferences that accommodate aversion to subjective uncertainty and its potential misspecification in dynamic settings are a valuable tool of analysis in many disciplines. By generalizing previous analyses, we propose a tractable approach to incorporating broadly conceived responses to uncertainty. We illustrate our approach on some stylized stochastic environments. By design, these discrete time environments have revealing continuous time limits. Drawing on these illustrations, we construct recursive representations of intertemporal preferences that allow for penalized and smooth ambiguity aversion to subjective uncertainty. These recursive representations imply continuous time limiting Hamilton-Jacobi-Bellman equations for solving control problems in the presence of uncertainty.
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http://dx.doi.org/10.1073/pnas.1811243115 | DOI Listing |
Biom J
December 2024
Department of Statistics, University of Haifa, Haifa, Israel.
Ordinary differential equations (ODEs) are foundational tools in modeling intricate dynamics across a gamut of scientific disciplines. Yet, a possibility to represent a single phenomenon through multiple ODE models, driven by different understandings of nuances in internal mechanisms or abstraction levels, presents a model selection challenge. This study introduces a testing-based approach for ODE model selection amidst statistical noise.
View Article and Find Full Text PDFStat Med
December 2024
School of Mathematics and Statistics, University of Melbourne, Parkville, Victoria, Australia.
We propose a semiparametric approach to Bayesian modeling of dynamic treatment regimes that is built on a Bayesian likelihood-based regression estimation framework. Methods based on this framework exhibit a probabilistic coherence property that leads to accurate estimation of the optimal dynamic treatment regime. Unlike most Bayesian estimation methods, our proposed method avoids strong distributional assumptions for the intermediate and final outcomes by utilizing empirical likelihoods.
View Article and Find Full Text PDFBehav Res Methods
December 2024
Institute of Psychology, Department of Research Methods and Evaluation, Goethe University Frankfurt, Theodor-W.-Adorno-Platz 6, 60629, Frankfurt am Main, Germany.
Closed-form (asymptotic) analytical power estimation is only available for limited classes of models, requiring correct model specification for most applications. Simulation-based power estimation can be applied in almost all scenarios where data following the model can be estimated. However, a general framework for calculating the required sample sizes for given power rates is still lacking.
View Article and Find Full Text PDFNeural Netw
December 2024
Department of Mathematics, UCLA, Los Angeles, CA 90024, USA. Electronic address:
Approximating nonlinear differential equations using a neural network provides a robust and efficient tool for various scientific computing tasks, including real-time predictions, inverse problems, optimal controls, and surrogate modeling. Previous works have focused on embedding dynamical systems into networks through two approaches: learning a single operator (i.e.
View Article and Find Full Text PDFNat Commun
September 2024
Department of Ecology and Evolution, University of Chicago, Chicago, IL, USA.
How complex are the rules by which a protein's sequence determines its function? High-order epistatic interactions among residues are thought to be pervasive, suggesting an idiosyncratic and unpredictable sequence-function relationship. But many prior studies may have overestimated epistasis, because they analyzed sequence-function relationships relative to a single reference sequence-which causes measurement noise and local idiosyncrasies to snowball into high-order epistasis-or they did not fully account for global nonlinearities. Here we present a reference-free method that jointly infers specific epistatic interactions and global nonlinearity using a bird's-eye view of sequence space.
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