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Semantics of higher-order probabilistic programs with conditioning

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arxiv 1902.11189 v1 pith:H7O7JWFU submitted 2019-02-28 cs.LO cs.LGcs.PL

classification cs.LOcs.LGcs.PL
keywords semanticsbanachhigher-orderprogramsspacesprobabilistictermsallow
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We present a denotational semantics for higher-order probabilistic programs in terms of linear operators between Banach spaces. Our semantics is rooted in the classical theory of Banach spaces and their tensor products, but bears similarities with the well-known Scott semantics of higher-order programs through the use ordered Banach spaces which allow definitions in terms of fixed points. Being based on a monoidal rather than cartesian closed structure, our semantics effectively treats randomness as a resource.

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  1. Dynamic ETF Portfolio Optimization Using enhanced Transformer-Based Models for Covariance and Semi-Covariance Prediction(Work in Progress)

    q-fin.PM 2024-11 reject novelty 4.0 of 10

    Transformer-based covariance and semi-covariance forecasts are claimed to improve ETF portfolio returns, but the supporting backtest is short, leaky, and unreproducible.

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