Learning Reduced Order Dynamics via Geometric Representations
Imran Nasim, Melanie Weber
SCML 2024
This paper presents two complementary but equivalent semantics for a high level probabilistic programming language. One of these interprets programs as partial measurable functions on a measurable space. The other interprets programs as continuous linear operators on a Banach space of measures. It is shown how the ordered domains of Scott and others are embedded naturally into these spaces. We use the semantics to prove a general result about probabilistic programs, namely, that a program's behavior is completely determined by its action on fixed inputs. © 1981.
Imran Nasim, Melanie Weber
SCML 2024
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Charles A Micchelli
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INFORMS 2022