Hasanov, Mahir2024-03-132024-03-1320232291-8639https://doi.org/10.28924/2291-8639-21-2023-57https://hdl.handle.net/20.500.12662/3823Let (X, Z) be a bivariate random vector. A predictor of X based on Z is just a Borel function g(Z). The problem of least squares prediction of X given the observation Z is to find the global minimum point of the functional E[(X - g(Z))2] with respect to all random variables g(Z), where g is a Borel function. It is well known that the solution of this problem is the conditional expectation E(X|Z). We also know that, if for a nonnegative smooth function F : RxR & RARR; R, arg ming(Z)E[F (X, g(Z))] = E[X|Z], for all X and Z, then F (x, y) is a Bregmann loss function. It is also of interest, for a fixed & phi; to find F (x, y ), satisfying, arg ming(Z)E[F (X, g(Z))] = & phi;(E[X|Z]), for all X and Z. In more general setting, a stronger problem is to find F (x, y) satisfying arg miny & ISIN;RE[F (X, y)] = & phi;(E[X]), & FORALL;X. We study this problem and develop a partial differential equation (PDE) approach to solution of these problems.eninfo:eu-repo/semantics/openAccessexpectationconditional expectationrandom variablesBregman loss functionspartial differential equationsArticle10.28924/2291-8639-21-2023-57Q421WOS:001021285600002N/A