- Tytuł:
- Bayesian multidimensional-matrix polynomial empirical regression
- Autorzy:
- Mukha, Vladimir S.
- Powiązania:
- https://bibliotekanauki.pl/articles/2050059.pdf
- Data publikacji:
- 2020
- Wydawca:
- Polska Akademia Nauk. Instytut Badań Systemowych PAN
- Tematy:
-
regression function
parameter estimation
maximum likelihood estimation
Bayesian estimation
multidimensional matrice - Opis:
- The problem of parameter estimation for the polynomial in the input variables regression function is formulated and solved. The input and output variables of the regression function are multidimensional matrices. The parameters of the regression function are assumed to be random independent multidimensional matrices with Gaussian distribution and known mean value and variance matrices. The solution to this problem is a multidimensional-matrix system of the linear algebraic equations in multidimensional-matrix unknown regression function parameters. We consider the particular cases of constant, affine and quadratic regression function, for which we have obtained formulas for parameter calculation. Computer simulation of the quadratic regression function is performed for the two-dimensional matrix input and output variables.
- Źródło:
-
Control and Cybernetics; 2020, 49, 3; 291--314
0324-8569 - Pojawia się w:
- Control and Cybernetics
- Dostawca treści:
- Biblioteka Nauki
Artykuł