標(biāo)題: Titlebook: Bayesian Methods for the Physical Sciences; Learning from Exampl Stefano Andreon,Brian Weaver Book 2015 Springer Nature Switzerland AG 2015 [打印本頁(yè)] 作者: 解放 時(shí)間: 2025-3-21 16:36
書(shū)目名稱(chēng)Bayesian Methods for the Physical Sciences影響因子(影響力)
書(shū)目名稱(chēng)Bayesian Methods for the Physical Sciences影響因子(影響力)學(xué)科排名
書(shū)目名稱(chēng)Bayesian Methods for the Physical Sciences網(wǎng)絡(luò)公開(kāi)度
書(shū)目名稱(chēng)Bayesian Methods for the Physical Sciences網(wǎng)絡(luò)公開(kāi)度學(xué)科排名
書(shū)目名稱(chēng)Bayesian Methods for the Physical Sciences被引頻次
書(shū)目名稱(chēng)Bayesian Methods for the Physical Sciences被引頻次學(xué)科排名
書(shū)目名稱(chēng)Bayesian Methods for the Physical Sciences年度引用
書(shū)目名稱(chēng)Bayesian Methods for the Physical Sciences年度引用學(xué)科排名
書(shū)目名稱(chēng)Bayesian Methods for the Physical Sciences讀者反饋
書(shū)目名稱(chēng)Bayesian Methods for the Physical Sciences讀者反饋學(xué)科排名
作者: Parameter 時(shí)間: 2025-3-21 22:37
Book 2015yriad of scenarios through slight modifications..This book is comprehensive, well written, and will surely be regarded as a standard text in both astrostatistics and physical statistics..Joseph M. Hilbe,. President, International Astrostatistics Association, Professor Emeritus, University of Hawaii,作者: gain631 時(shí)間: 2025-3-22 02:43
Bayesian Methods for the Physical Sciences978-3-319-15287-5Series ISSN 2199-1030 Series E-ISSN 2199-1049 作者: THROB 時(shí)間: 2025-3-22 07:48 作者: 培養(yǎng) 時(shí)間: 2025-3-22 10:11 作者: muffler 時(shí)間: 2025-3-22 16:40
Human Enhancements for Space Missionsvents or objects are over–represented in samples and difficult–to–collect are under–represented if not missing altogether. In this chapter we show how to account for non–random data collection to infer the properties of the population from the studied sample.作者: 謙虛的人 時(shí)間: 2025-3-22 17:34 作者: Spinal-Tap 時(shí)間: 2025-3-23 00:48 作者: neolith 時(shí)間: 2025-3-23 01:59
The Emotions in Life and Science,This chapter presents some basic steps for performing a good statistical analysis, all summarized in about one page.作者: 粗魯性質(zhì) 時(shí)間: 2025-3-23 09:13 作者: 圣人 時(shí)間: 2025-3-23 10:52
Daniel B. Diner,Derek H. FenderThis chapter introduces the computational tools and methods that we use for sampling from the posterior distribution. Since all numerical computations, and Bayesian ones are no exception, may end in errors, we also provide a few tips to check that the numerical computation is sampling from the posterior distribution.作者: floodgate 時(shí)間: 2025-3-23 16:31 作者: 詞匯表 時(shí)間: 2025-3-23 19:23 作者: mitral-valve 時(shí)間: 2025-3-24 01:01
A Bit of Theory,This short chapter introduces the basics of probability theory in an intuitive fashion using simple examples. It also illustrates, again with examples, how to propagate errors and the difference between marginal and profile likelihoods.作者: Vulnerable 時(shí)間: 2025-3-24 06:26 作者: 注入 時(shí)間: 2025-3-24 07:20 作者: MITE 時(shí)間: 2025-3-24 14:21
https://doi.org/10.1007/978-3-319-15287-5Bayesian astrostatistics; Bayesian methods for astronomy; Fitting regression models in physical scienc作者: SSRIS 時(shí)間: 2025-3-24 16:12
978-3-319-36783-5Springer Nature Switzerland AG 2015作者: RAFF 時(shí)間: 2025-3-24 19:06
Evolving from Earthlings into Martians?ptions). We illustrate this concept with examples where the prior plays greatly different roles, from major to negligible. We also provide some advice on how to look for information useful for sculpting the prior.作者: 分開(kāi)如此和諧 時(shí)間: 2025-3-24 23:56 作者: 看法等 時(shí)間: 2025-3-25 07:05 作者: 態(tài)度暖昧 時(shí)間: 2025-3-25 08:36 作者: 斜 時(shí)間: 2025-3-25 11:48 作者: 一夫一妻制 時(shí)間: 2025-3-25 17:12 作者: orthodox 時(shí)間: 2025-3-25 21:22
The Prior,ptions). We illustrate this concept with examples where the prior plays greatly different roles, from major to negligible. We also provide some advice on how to look for information useful for sculpting the prior.作者: 懶洋洋 時(shí)間: 2025-3-26 01:35 作者: 曲解 時(shí)間: 2025-3-26 07:54
Non-random Data Collection,vents or objects are over–represented in samples and difficult–to–collect are under–represented if not missing altogether. In this chapter we show how to account for non–random data collection to infer the properties of the population from the studied sample.作者: fatty-streak 時(shí)間: 2025-3-26 10:15
Fitting Regression Models,ment errors of different amplitudes and an intrinsic variety in the studied populations, or an extra source of variability? A number of examples illustrate how to answer these questions and how to predict the value of an unavailable quantity by exploiting the existence of a trend with another, available, quantity.作者: 編輯才信任 時(shí)間: 2025-3-26 16:02
Book 2015 the tools they will need to analyze their own data. Chapters in this book provide a statistical base from which to approach new problems, including numerical advice and a profusion of examples. The examples are engaging analyses of real-world problems taken from modern astronomical research. The ex作者: intertwine 時(shí)間: 2025-3-26 18:17 作者: transdermal 時(shí)間: 2025-3-27 00:38 作者: Obsequious 時(shí)間: 2025-3-27 02:44
Carsten Carlberg,Ferdinand Molnár some specific complexity pointed out by the data? Furthermore, are the data informative about the quantity being measured or are results sensibly dependent on details of the fitted model? And, finally, what about if assumptions are uncertain? A number of examples illustrate how to answer these questions.作者: 割公牛膨脹 時(shí)間: 2025-3-27 07:18 作者: Metastasis 時(shí)間: 2025-3-27 09:27 作者: STENT 時(shí)間: 2025-3-27 16:46
Bayesian vs Simple Methods,n terms of quality of the prediction, accuracy of the estimates, and fairness and noisiness of the quoted errors. We also focus on three failures of maximum likelihood methods occurring with small samples, with mixtures, and with regressions with errors in the predictor quantity.作者: HATCH 時(shí)間: 2025-3-27 21:35 作者: 正常 時(shí)間: 2025-3-27 22:13 作者: 錯(cuò)事 時(shí)間: 2025-3-28 03:49 作者: 陳舊 時(shí)間: 2025-3-28 10:19
Fitting Regression Models,ip and estimate any unknown parameters that dictate this relationship. Questions of interest include: how to deal with samples affected by selection effects? How does a rich data structure influence the fitted parameters? And what about non-linear multiple-predictor fits, upper/lower limits, measure作者: CHOIR 時(shí)間: 2025-3-28 11:06 作者: 戰(zhàn)役 時(shí)間: 2025-3-28 16:40 作者: Tidious 時(shí)間: 2025-3-28 22:28
10樓作者: 植物學(xué) 時(shí)間: 2025-3-28 22:54
10樓
