A New Approach to the Construction of the APF Algorithm by Applying the Pearson Curves Technique
PBN-AR
Instytucja
Wydział Zarządzania i Modelowania Komputerowego (Politechnika Świętokrzyska)
Informacje podstawowe
Główny język publikacji
en
Czasopismo
Applied Mathematics & Information Sciences
ISSN
1935-0090
EISSN
Wydawca
DOI
URL
Rok publikacji
2016
Numer zeszytu
3
Strony od-do
1-8
Numer tomu
10
Link do pełnego tekstu
Identyfikator DOI
Liczba arkuszy
Autorzy
(liczba autorów: 2)
Pozostali autorzy
+ 1
Słowa kluczowe
en
Sequential Monte Carlo methods
state-space models
stochastic volatility process SV
Pearsons curves technique
Streszczenia
Język
en
Treść
We consider the theoretical question concerning time series which arises when the distribution of the observed variable is in fact a conditional distribution. The Kalman filter provides an effective solution to the linear Gaussian filtering problem. However, when state or measurement, or both, are highly non-linear, and posterior probability distribution of the state is non-Gaussian, the optimal linear filter and its modifications do not provide satisfactory results. The Sequential Monte Carlo method (SMC) have become one of the familiar tools that allowed the Bayesian paradigm to be applied to approximation of sophisticated models. In this paper we propose a novel construction of an auxiliary particle filter (APF) algorithm using the Pearson curves technique (PC) for approximation of importance weights of simulated particles. The effectiveness of the method is discussed and illustrated by numerical results based on the simulated stochastic volatility process SV.
Cechy publikacji
peer-reviewed
Inne
System-identifier
42150