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Reasoning about Uncertainties in Discrete-Time Dynamical Systems using Polynomial Forms

Abstract : In this paper, we propose polynomial forms to represent distributions of state variables over time for discrete-time stochastic dynamical systems. This problem arises in a variety of applications in areas ranging from biology to robotics. Our approach allows us to rigorously represent the probability distribution of state variables over time, and provide guaranteed bounds on the expectations, moments and probabilities of tail events involving the state variables. First, we recall ideas from interval arithmetic, and use them to rigorously represent the state variables at time t as a function of the initial state variables and noise symbols that model the random exogenous inputs encountered before time t. Next, we show how concentration of measure inequalities can be employed to prove rigorous bounds on the tail probabilities of these state variables. We demonstrate interesting applications that demonstrate how our approach can be useful in some situations to establish mathematically guaranteed bounds that are of a different nature from those obtained through simulations with pseudo-random numbers.
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Contributor : Sylvie Putot Connect in order to contact the contributor
Submitted on : Wednesday, May 26, 2021 - 3:56:47 PM
Last modification on : Tuesday, May 10, 2022 - 3:40:56 AM
Long-term archiving on: : Friday, August 27, 2021 - 8:16:58 PM


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  • HAL Id : hal-03237392, version 1



Sriram Sankaranarayanan, Yi Chou, Eric Goubault, Sylvie Putot. Reasoning about Uncertainties in Discrete-Time Dynamical Systems using Polynomial Forms. 2020 Conference on Neural Information Processing Systems (NeurIPS 2020), Dec 2020, Online, France. ⟨hal-03237392⟩



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