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The problem of estimating underlying trends in time series data arises in a variety of disciplines. In this paper we propose a variation on Hodrick-Prescott (H-P) filtering, a widely used method for t...
The least squares method allows fitting parameters of a mathematical model from experimental data. This article proposes a general approach of this method. After introducing the method and giving a fo...
This paper introduces a spatio-temporal resonator model and an inference method for detection and estimation of nearly periodic temporal phenomena in spatio-temporal data. The model is derived as a sp...
We introduce an auxiliary technique, called residual nudging, to the particle filter to enhance its performance in cases that it performs poorly. The main idea of residual nudging is to monitor, and i...
Decision tree learning is a popular approach for classification and regression in machine learning and statistics, and Bayesian formulations---which introduce a prior distribution over decision trees,...
We examine a general multi-factor model for commodity spot prices and futures valuation. We extend the multi-factor long-short model in [1] and [2] in two important aspects: firstly we allow for both...
The problem of filtering of finite--alphabet stationary ergodic time series is considered. A method for constructing a confidence set for the (unknown) signal is proposed, such that the resulting set ...
We examine the problem of optimal design in the context of filtering multiple random walks. Specifically, we define the steady state E-optimal design criterion and show that the underlying optimizatio...
We describe a novel framework for learning recommender models for recommendation systems, which views user-system-item interactions as an opportunity give-and-take process, and encodes both "collabora...
In the frame of time series analysis, we compute the quadratic error in the blind estimation of the projection operator for prediction with infinite past. The estimation is made using only a single ...
A method of ‘network filtering’ has been proposed recently to detect the effects of certain external perturbations on the interacting members in a network. However, with large networks, the goal of de...
The control process that minimizes the quadratic performance functional associated with a quantum system whose evolution is described by a Hudson-Parthasarathy type stochastic differential equation...
This article considers the application of particle ltering to continuous- discrete optimal ltering problems, where the system model is a stochastic dier- ential equation, and noisy measurements of t...
In this paper, we prove, using Malliavin calculus, that under a global Hormander condition the law of a Riemannian manifold valued stochastic process, a solution of a stochastic differential equati...
When is a nonlinear filter stable with respect to its initial condition? In spite of the recent progress, this question still lacks a complete answer in general. Currently available results indicate t...

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