5 papers
Adaptive Learning via Off-Model Training and Importance Sampling for Fully Non-Markovian Optimal Stochastic Control. Complete version
Dorival Leão, Alberto Ohashi, Simone Scotti +1
This paper studies continuous-time stochastic control problems whose controlled states are fully non-Markovian and depend on unknown model parameters. Such problems arise naturally…
Forward stochastic integration for adapted processes w.r.t. Riemann-Liouville fractional Brownian motion (Full version)
Paulo Henrique da Costa, Alberto Ohashi, Francesco Russo
This paper provides the time-dependent -martingale representation of the forward stochastic integral where the driving noise is the Riemann-Liouville fractional Brownian motio…
About semilinear low dimension Bessel PDEs
Alberto Ohashi, Francesco Russo, Alan Teixeira
We prove existence and uniqueness of solutions of a semilinear PDE driven by a Bessel type generator with low dimension . is a local operator, whose drift is t…
The -norm of the forward stochastic integral w.r.t. Fractional Brownian motion
Alberto Ohashi, Francesco Russo
In this article, we present the exact expression of the -norm of the forward stochastic integral driven by the multi-dimensional fractional Brownian motion with parameter $\fr…
The isometry of symmetric-Stratonovich integrals w.r.t. Fractional Brownian motion
Alberto Ohashi, Francesco Russo, Frederi Viens
In this work, we present a detailed analysis on the exact expression of the -norm of the symmetric-Stratonovich stochastic integral driven by a multi-dimensional fractional Br…