Optimal Design for Estimation in Diffusion Processes from First Hitting Times

DOI10.1137/16M1060376zbMath1365.62305OpenAlexW2577287874MaRDI QIDQ5269855

Alexandre Iolov, Susanne Ditlevsen, André Longtin

Publication date: 28 June 2017

Published in: SIAM/ASA Journal on Uncertainty Quantification (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1137/16m1060376

zbMATH Keywords

design of experimentsFokker-Planck equationmutual informationOrnstein-Uhlenbeck processleaky integrate-and-fire neuronal modelsprobability density function control method

Mathematics Subject Classification ID

Optimal statistical designs (62K05) Dynamic programming in optimal control and differential games (49L20) Neural biology (92C20) Population dynamics (general) (92D25) Optimal stochastic control (93E20) Diffusion processes (60J60) Discrete approximations in optimal control (49M25)

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Experimental Design for Partially Observed Markov Decision Processes ⋮ Approximating the first passage time density from data using generalized Laguerre polynomials ⋮ Uncertainty Quantification of Unsteady Flows Generated by Line-Sources Through Heterogeneous Geological Formations ⋮ Transient dynamics of Pearson diffusions facilitates estimation of rate parameters ⋮ Inference of a Mesoscopic Population Model from Population Spike Trains ⋮ Optimal control for estimation in partially observed elliptic and hypoelliptic linear stochastic differential equations ⋮ A Fokker-Planck control framework for stochastic systems ⋮ Timing observations of diffusions ⋮ Optimal control for parameter estimation in partially observed hypoelliptic stochastic differential equations

Cites Work

• Unnamed Item
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• A Fokker-Planck control framework for multidimensional stochastic processes
• A review of the methods for signal estimation in stochastic diffusion leaky integrate-and-fire neuronal models
• Fokker-Planck and Fortet equation-based parameter estimation for a leaky integrate-and-fire model with sinusoidal and stochastic forcing
• A tutorial on adaptive design optimization
• Control Theory and Experimental Design in Diffusion Processes
• OPTIMAL CONTROL OF PROBABILITY DENSITY FUNCTIONS OF STOCHASTIC PROCESSES
• Asymptotic Theory of Information-Theoretic Experimental Design
• Computational Optimization of Systems Governed by Partial Differential Equations
• Adaptive Design Optimization: A Mutual Information-Based Approach to Model Discrimination in Cognitive Science
• Sequential Optimal Design of Neurophysiology Experiments
• Introduction to Theoretical Neurobiology
• Numerical Optimization
• Maximum Likelihood Estimation of a Stochastic Integrate-and-Fire Neural Encoding Model
• Robust online Hamiltonian learning
• Elements of Information Theory

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