forbid
发表于 2025-3-23 12:08:14
Single Stationary Sinusoid Plus Noise,frequencies and more complex models; second, to introduce a different prior probability for the amplitudes, which simplifies the calculation but has almost no effect on the final result; and third, to introduce and discuss the calculation techniques without the complex model functions confusing the issues.
Noctambulant
发表于 2025-3-23 13:52:48
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小木槌
发表于 2025-3-23 19:32:52
0930-0325 dition, I must extend my deepest thanks to Dr. Joseph Ackerman for his support during the time this manuscript was being prepared.978-0-387-96871-1978-1-4684-9399-3Series ISSN 0930-0325 Series E-ISSN 2197-7186
积极词汇
发表于 2025-3-24 00:22:27
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Reservation
发表于 2025-3-24 04:48:41
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地名表
发表于 2025-3-24 06:35:44
Introduction,be analyzed. These three steps are highly idealized, and no clear boundary exists between them. The problem of analyzing the data is one that should be faced early in the design phase. Gathering the data in such a way as to learn the most about a phenomenon is what doing an experiment is all about.
FIG
发表于 2025-3-24 13:18:18
Single Stationary Sinusoid Plus Noise,the model parameters which concerns us here. The time series .(t) we are considering is postulated to contain a single stationary harmonic signal .(t) plus noise .(t). The basic model is always: we have recorded a discrete data set . = {.., ⋅⋅⋅, ..}; sampled from .(t) at discrete times {.., ⋅⋅⋅, ..}
租约
发表于 2025-3-24 18:15:51
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Aqueous-Humor
发表于 2025-3-24 19:19:34
Model Selection,e models. This is easily done using Bayes’ theorem (1.3) and repeated applications of the procedure (1.4) which led to the “Student t-distribution.” The first step in answering this question is to enumerate the possible models. Suppose we have a set of . possible models {.., ⋅⋅⋅, ..} with model func
Badger
发表于 2025-3-25 00:47:37
Spectral Estimation,im is to derive explicit Bayesian estimates of the power spectrum and other parameters when multiple nonstationary frequencies are present. We will do this by proceeding through several stages beginning with the simplest spectrum estimation problem. We do this because as was shown by Jaynes whe