C:/Documents and Settings/campillo/Mes documents/1-work/2006-06-RR-parameter-vivien/squelette.dvi
نویسنده
چکیده
The state-space modeling of partially observed dynamic systems generally requires estimates of unknown parameters. From a practical point of view, it is relevant in such filtering contexts to simultaneously estimate the unknown states and parameters. Efficient simulation-based methods using convolution particle filters are proposed. The regularization properties of these filters is well suited, given the context of parameter estimation. Firstly the usual non Bayesian statistical estimates are considered: the conditional least squares estimate (CLSE) and the maximum likelihood estimate (MLE). Secondly, in a Bayesian context, a Monte Carlo type method is presented. Finally these methods are compared in several simulated case studies. Key-words: Hidden Markov models, parameter estimation, particle filter, convolution kernels, conditional least squares estimate, maximum likelihood estimate
منابع مشابه
C:/Documents and Settings/imb/Mes documents/yger/CoursYger/CoursMHT613new/ExamMHT613corr-0708.dvi
Exercice (6 pts) Soient ḟ et ġ deux éléments de L C (T, dθ). Vérifier que, pour tout N ∈ N, la fonction gN : θ 7−→ gN(θ) := g(Nθ) définit encore un élément de L C (T, dθ) (1 pt). Si g est un représentant de ġ (i.e une fonction mesurable 2π-périodique telle que |g|2 soit intégrable sur [0, 2π] et que g ∈ ġ), la fonction gN : θ 7−→ g(Nθ) est encore 2π-périodique car gN(θ + 2π) = g(N(θ + 2π)) = g(...
متن کاملA Multitask Learning Approach to Document Representation using Unlabeled Data
Text categorization is intrinsically a supervised learning task, which aims at relating a given text document to one or more predefined categories. Unfortunately, labeling such databases of documents is a painful task. We present in this paper a method that takes advantage of huge amounts of unlabeled text documents available in digital format, to counter balance the relatively smaller availabl...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2006