標(biāo)題: Titlebook: Maximum-Likelihood Deconvolution; A Journey into Model Jerry M. Mendel Book 1990 Springer-Verlag New York Inc. 1990 Signal.entropy.filter.f [打印本頁] 作者: 弄碎 時(shí)間: 2025-3-21 16:22
書目名稱Maximum-Likelihood Deconvolution影響因子(影響力)
作者: PAGAN 時(shí)間: 2025-3-21 22:14
Introduction,of convolution that tells us how to compute the output of a LTI system from its input and impulse response (IR), i.e.,. where * denotes the mathematical operation of convolution. Convolution is associated with the “forward problem” of generating the responce of a LTI system from known values of its input and IR.作者: saphenous-vein 時(shí)間: 2025-3-22 01:12 作者: 以煙熏消毒 時(shí)間: 2025-3-22 07:49 作者: 吸引力 時(shí)間: 2025-3-22 10:19 作者: fatuity 時(shí)間: 2025-3-22 13:55 作者: GROUP 時(shí)間: 2025-3-22 19:34
Mathematical Details for Chapter 4,This chapter is for the mathematically serious reader. In it we quantify many of the previous qualitative statements that were made in Chapter 4. For the convenience of the reader, we restate many of the Chapter 4 results, prior to their derivations.作者: BIPED 時(shí)間: 2025-3-22 23:51
Mathematical Details for Chapter 5,This chapter is similar in spirit to Chapter 7. It provides the mathematical details for many of the statements that were made, without proof, in Chapter 5. For convenience of the reader, we again state many of the Chapter 5 results, prior to their derivations.作者: 新娘 時(shí)間: 2025-3-23 01:27 作者: LIKEN 時(shí)間: 2025-3-23 07:14
Signal Processing and Digital Filteringhttp://image.papertrans.cn/m/image/627924.jpg作者: 慢跑鞋 時(shí)間: 2025-3-23 12:40
1431-7893 stimation theory, are unnecessary to understand the essence of MLD. MLD is a model-based signal processing procedure, because it is based on a signal model, nam978-1-4612-7985-3978-1-4612-3370-1Series ISSN 1431-7893 作者: Stagger 時(shí)間: 2025-3-23 15:54 作者: INERT 時(shí)間: 2025-3-23 21:46
Book 1990elatively simple ideas from optimization theory. Earlier approaches to MLD, which are couched in the language of state-variable models and estimation theory, are unnecessary to understand the essence of MLD. MLD is a model-based signal processing procedure, because it is based on a signal model, nam作者: extemporaneous 時(shí)間: 2025-3-24 01:21 作者: accomplishment 時(shí)間: 2025-3-24 02:28
Jerry M. Mendelprocessors provided an understanding of the specifications, operations and analysis required in the optical/digital interface. The interface used in the hybrid system at Carnegie-Mellon University was discussed in considerable detail (Sect. 5.5) with operations, hardware, and software included. A se作者: FRONT 時(shí)間: 2025-3-24 09:28
Jerry M. Mendelprocessors provided an understanding of the specifications, operations and analysis required in the optical/digital interface. The interface used in the hybrid system at Carnegie-Mellon University was discussed in considerable detail (Sect. 5.5) with operations, hardware, and software included. A se作者: 貞潔 時(shí)間: 2025-3-24 14:29 作者: 坦白 時(shí)間: 2025-3-24 16:51
Jerry M. Mendelprocessors provided an understanding of the specifications, operations and analysis required in the optical/digital interface. The interface used in the hybrid system at Carnegie-Mellon University was discussed in considerable detail (Sect. 5.5) with operations, hardware, and software included. A se作者: condemn 時(shí)間: 2025-3-24 20:03
Jerry M. Mendelprocessors provided an understanding of the specifications, operations and analysis required in the optical/digital interface. The interface used in the hybrid system at Carnegie-Mellon University was discussed in considerable detail (Sect. 5.5) with operations, hardware, and software included. A se作者: 砍伐 時(shí)間: 2025-3-25 03:01 作者: Narcissist 時(shí)間: 2025-3-25 03:41 作者: 不愛防注射 時(shí)間: 2025-3-25 08:34 作者: 代替 時(shí)間: 2025-3-25 13:03 作者: dura-mater 時(shí)間: 2025-3-25 19:31
Jerry M. MendelThe diffraction pattern sampling system (Sect. 5.2) is the most developed and hardened but is also the simplest and least powerful. The optical preprocessor system (Sect. 5.3) uses extensive digital analysis of optical light distributions (usually Fourier transform planes or texture variance images)作者: 極少 時(shí)間: 2025-3-25 22:32 作者: Aboveboard 時(shí)間: 2025-3-26 02:34 作者: 傲慢人 時(shí)間: 2025-3-26 07:16 作者: Paradox 時(shí)間: 2025-3-26 11:43 作者: 擔(dān)心 時(shí)間: 2025-3-26 14:37
Likelihood,22 and 1925) developed the method of maximum likelihood for problems that are characterized just by deterministic parameters. Another method, associated with the name of Thomas Bayes, called the Maximum a Posteriori Method, i.e., MAP (e.g., Sorenson, 1980, and Mendel, 1987a), was developed for probl作者: 冒號(hào) 時(shí)間: 2025-3-26 18:41
Maximizing Likelihood,here either the likelihood function L{. ? .} or the loglikelihood function .{. ? .} attains its maximum. Because of the exponential nature of our likelihood function, we shall focus on maximizing . {}. Maximum-likelihood values of the parameter vectors are denoted with a superscript ML, e.g., ., ...作者: somnambulism 時(shí)間: 2025-3-26 22:41
Properties and Performance,ar signal processing; and, ., which is also a form of nonlinear signal processing. In this chapter we describe everything that is known to-date about the properties and performance of these three types of signal processing. We do this in order to gain a better appreciation and understanding of these作者: fledged 時(shí)間: 2025-3-27 02:01
Examples,terial in Chapters 4 and 5. Real and synthetic data cases are presented, because much can be learned about MLD from both types of data. Rather than collect all of the real data examples in one section, at the end of the chapter, as is customarily done in journal articles, we shall weave them in with作者: Basilar-Artery 時(shí)間: 2025-3-27 09:17
Maximizing Likelihood,lihood function, we shall focus on maximizing . {}. Maximum-likelihood values of the parameter vectors are denoted with a superscript ML, e.g., ., ... There are many different methods one can use to maximize .{}. We shall examine some of these in this chapter. First, however, we must be convinced that maximizing .{} is a meaningful thing to do.作者: Notorious 時(shí)間: 2025-3-27 11:18 作者: Hamper 時(shí)間: 2025-3-27 14:42
1431-7893 raveling of convolution. It is the inverse problem of generating the system‘s input from knowledge about the system‘s output and dynamics. Deconvolution requires a careful balancing of bandwidth and signal-to-noise ratio effects. Maximum-likelihood deconvolution (MLD) is a design procedure that hand作者: 較早 時(shí)間: 2025-3-27 19:36
Book 1990f convolution. It is the inverse problem of generating the system‘s input from knowledge about the system‘s output and dynamics. Deconvolution requires a careful balancing of bandwidth and signal-to-noise ratio effects. Maximum-likelihood deconvolution (MLD) is a design procedure that handles both e作者: 領(lǐng)巾 時(shí)間: 2025-3-28 00:40 作者: 工作 時(shí)間: 2025-3-28 05:22 作者: flutter 時(shí)間: 2025-3-28 07:51 作者: 語源學(xué) 時(shí)間: 2025-3-28 12:58
George E. Yoosoper should know. The book is simple and concise, giving readers an immediate return on their investment. After learning the lessons of this book, business process analysts and developers will be prepared to us978-1-4302-2751-9978-1-4302-2752-6作者: Cardiac-Output 時(shí)間: 2025-3-28 16:20 作者: 和諧 時(shí)間: 2025-3-28 18:52
https://doi.org/10.1007/978-3-031-05017-6art education; higher education; industry; creative industries; neoliberalism作者: Prologue 時(shí)間: 2025-3-28 23:36
