北极人
发表于 2025-3-30 11:02:42
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灾祸
发表于 2025-3-30 13:22:19
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Vertebra
发表于 2025-3-30 18:04:23
The Smoothness Priors Concept,0a) in which the framework initiated by Shiller was continued Akaike (1980a) was a quasi-Bayesian Gaussian disturbances linear regression, least squares computations, model framework. Stochastic difference equation constraints were placed on the prior distributions of the model parameters. The criti
GLUT
发表于 2025-3-30 23:04:38
Scalar Least Squares Modeling,linear Gaussian modeling. Smoothness priors trend estimation for scalar time series is treated in Section 4.1. There, the smoothness priors constraint is expressed as a k-th order random walk with a normally distributed zero-mean, unknown variance perturbation. The (normalized) variance is a hyperpa
jettison
发表于 2025-3-31 03:18:08
Linear Gaussian State Space Modeling,own. Model identification or, computation of the likelihood of the model is also treated. Some of the well known state space models that are used in this book as well as state space modeling of missing observations and a state space model for unequally spaced time series are shown. The final section
Ophthalmoscope
发表于 2025-3-31 05:49:58
General State Space Modeling,because of the need to model time series with abrupt discontinuities, and time series with outliers and to model time series whose state and or observation processes were nonlinear. The general state space model and its recursive formulas for prediction, filtering and smoothing are treated in Sectio
两种语言
发表于 2025-3-31 13:03:03
Applications of Linear Gaussian State Space Modeling,Canadian lynx data by an AR state space model, the modeling of irregularly spaced data and an example of the decomposition of an observed time series into a signal, background noise and observation noise are shown.
得罪人
发表于 2025-3-31 13:42:11
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