標(biāo)題: Titlebook: All of Statistics; A Concise Course in Larry Wasserman Textbook 2004 Springer Science+Business Media, LLC, part of Springer Nature 2004 Bo [打印本頁] 作者: 遠見 時間: 2025-3-21 19:48
書目名稱All of Statistics影響因子(影響力)
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書目名稱All of Statistics讀者反饋
書目名稱All of Statistics讀者反饋學(xué)科排名
作者: AVID 時間: 2025-3-21 23:31
Ontology-Based Multimedia Reasoning, .. The basic question is this: what can we say about the limiting behavior of a sequence of random variables .., .., ..,...? Since statistics and data mining are all about gathering data, we will naturally be interested in what happens as we gather more and more data.作者: companion 時間: 2025-3-22 04:13 作者: Allergic 時間: 2025-3-22 06:04 作者: 歌劇等 時間: 2025-3-22 08:45
Causal Inferencesociated but the reverse is not, in general, true. Association does not necessarily imply causation. We will consider two frameworks for discussing causation. The first uses counterfactual random variables. The second, presented in the next chapter, uses directed acyclic graphs.作者: Resign 時間: 2025-3-22 13:37
https://doi.org/10.1007/978-0-387-21736-9Bootstrapping; Mathematica; ROOT; Random variable; STATISTICA; classification; data mining; machine learnin作者: Immunization 時間: 2025-3-22 18:28
978-1-4419-2322-6Springer Science+Business Media, LLC, part of Springer Nature 2004作者: GNAT 時間: 2025-3-22 21:39 作者: 外觀 時間: 2025-3-23 02:46 作者: 移動 時間: 2025-3-23 08:30
https://doi.org/10.1007/978-981-13-3678-2The mean, or expectation, of a random variable . is the average value of ..作者: hurricane 時間: 2025-3-23 10:36 作者: 價值在貶值 時間: 2025-3-23 14:02
https://doi.org/10.1007/978-3-658-44144-9Statistical inference, or “l(fā)earning” as it is called in computer science, is the process of using data to infer the distribution that generated the data. A typical statistical inference question is:作者: 外形 時間: 2025-3-23 21:55 作者: 煞費苦心 時間: 2025-3-23 23:57 作者: 使混合 時間: 2025-3-24 05:56 作者: aerobic 時間: 2025-3-24 10:36
Determinism and Possible Worlds,Suppose we want to know if exposure to asbestos is associated with lung disease. We take some rats and randomly divide them into two groups. We expose one group to asbestos and leave the second group unexposed. Then we compare the disease rate in the two groups. Consider the following two hypotheses:作者: 輕浮思想 時間: 2025-3-24 11:29 作者: originality 時間: 2025-3-24 17:58
https://doi.org/10.1007/978-94-015-3692-9We have considered several point estimators such as the maximum likelihood estimator, the method of moments estimator, and the posterior mean. In fact, there are many other ways to generate estimators. How do we choose among them? The answer is found in . which is a formal theory for comparing statistical procedures.作者: HALL 時間: 2025-3-24 20:28
Most Complex Non-returning Regular Languages is a method for studying the relationship between a . Y and a . The covariate is also called a . or a ..作者: 小爭吵 時間: 2025-3-25 00:19
Branching Measures and Nearly Acyclic NFAsIn this chapter we revisit the Multinomial model and the multivariate Normal. Let us first review some notation from linear algebra. In what follows, . and . are vectors and . is a matrix.作者: MIME 時間: 2025-3-25 03:34 作者: 圍裙 時間: 2025-3-25 10:24
ProbabilityProbability is a mathematical language for quantifying uncertainty. In this Chapter we introduce the basic concepts underlying probability theory. We begin with the sample space, which is the set of possible outcomes.作者: inhibit 時間: 2025-3-25 12:44 作者: 可耕種 時間: 2025-3-25 16:22 作者: Externalize 時間: 2025-3-25 23:19
InequalitiesInequalities are useful for bounding quantities that might otherwise be hard to compute. They will also be used in the theory of convergence which is discussed in the next chapter. Our first inequality is Markov’s inequality.作者: zonules 時間: 2025-3-26 01:28
Models, Statistical Inference and LearningStatistical inference, or “l(fā)earning” as it is called in computer science, is the process of using data to infer the distribution that generated the data. A typical statistical inference question is:作者: MEEK 時間: 2025-3-26 06:40
Estimating the CDF and Statistical FunctionalsThe first inference problem we will consider is nonparametric estimation of the CDF .. Then we will estimate statistical functionals, which are functions of CDF, such as the mean, the variance, and the correlation. The nonparametric method for estimating functionals is called the plug-in method.作者: 保存 時間: 2025-3-26 11:03
The BootstrapThe . is a method for estimating standard errors and computing confidence intervals. Let .. = .(..,…, ..) be a ., that is, .. is any function of the data. Suppose we want to know V.(..), the variance of ... We have written V. to emphasize that the variance usually depends on the unknown distribution function ..作者: forestry 時間: 2025-3-26 12:38 作者: 認為 時間: 2025-3-26 18:46
Hypothesis Testing and p-valuesSuppose we want to know if exposure to asbestos is associated with lung disease. We take some rats and randomly divide them into two groups. We expose one group to asbestos and leave the second group unexposed. Then we compare the disease rate in the two groups. Consider the following two hypotheses:作者: 喃喃而言 時間: 2025-3-26 23:10
Bayesian InferenceThe statistical methods that we have discussed so far are known as . methods. The frequentist point of view is based on the following postulates:作者: 裝勇敢地做 時間: 2025-3-27 03:01 作者: choroid 時間: 2025-3-27 07:01 作者: 除草劑 時間: 2025-3-27 13:28 作者: Geyser 時間: 2025-3-27 13:37
Inference About IndependenceIn this chapter we address the following questions:作者: 現(xiàn)實 時間: 2025-3-27 21:35
Larry WassermanProvides a concise introduction to a larger number of topics than are usually included in a graduate-level mathematical statistics class作者: 衍生 時間: 2025-3-28 00:17
Springer Texts in Statisticshttp://image.papertrans.cn/a/image/153420.jpg作者: Brain-Waves 時間: 2025-3-28 02:22
All of Statistics978-0-387-21736-9Series ISSN 1431-875X Series E-ISSN 2197-4136 作者: originality 時間: 2025-3-28 09:34
Ontology-Based Multimedia Reasoning, .. The basic question is this: what can we say about the limiting behavior of a sequence of random variables .., .., ..,...? Since statistics and data mining are all about gathering data, we will naturally be interested in what happens as we gather more and more data.作者: maculated 時間: 2025-3-28 12:10 作者: 不如屎殼郎 時間: 2025-3-28 18:33
Convergence of Random Variables .. The basic question is this: what can we say about the limiting behavior of a sequence of random variables .., .., ..,...? Since statistics and data mining are all about gathering data, we will naturally be interested in what happens as we gather more and more data.作者: infatuation 時間: 2025-3-28 22:20
Causal Inferencesociated but the reverse is not, in general, true. Association does not necessarily imply causation. We will consider two frameworks for discussing causation. The first uses counterfactual random variables. The second, presented in the next chapter, uses directed acyclic graphs.作者: Eeg332 時間: 2025-3-29 01:22
Textbook 2004ics than a typical introductory book on mathematical statistics. This book is for people who want to learn probability and statistics quickly. It is suitable for graduate or advanced undergraduate students in computer science, mathematics, statistics, and related disciplines.?.The book includes mode作者: compose 時間: 2025-3-29 03:16
Textbook 2004is presumed to know calculus and a little linear algebra. No previous knowledge of probability and statistics is required. Statistics, data mining, and machine learning are all concerned with collecting and analysing data.?.作者: Mirage 時間: 2025-3-29 10:52
1431-875X n literally, the title "All of Statistics" is an exaggeration. But in spirit, the title is apt, as the book does cover a much broader range of topics than a typical introductory book on mathematical statistics. This book is for people who want to learn probability and statistics quickly. It is suita作者: 碌碌之人 時間: 2025-3-29 12:11
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