標題: Titlebook: Bayesian Statistical Modeling with Stan, R, and Python; Kentaro Matsuura Book 2022 Springer Nature Singapore Pte Ltd. 2022 Stan.Bayesian M [打印本頁] 作者: 不服從 時間: 2025-3-21 19:27
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書目名稱Bayesian Statistical Modeling with Stan, R, and Python被引頻次
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書目名稱Bayesian Statistical Modeling with Stan, R, and Python讀者反饋
書目名稱Bayesian Statistical Modeling with Stan, R, and Python讀者反饋學科排名
作者: 強所 時間: 2025-3-21 23:10 作者: 無表情 時間: 2025-3-22 02:45 作者: deficiency 時間: 2025-3-22 04:54
Vaccine Development for Cytomegalovirusyze spatial data.?It has a wide range of application and can be applied to one-dimensional data, two-dimensional grid data type, and geospatial map data. Later, we will see how a Gaussian process (GP) can be considered as a generalization of a GMRF. A GP can represent smooth functions, and usually gives high prediction performance.作者: 動物 時間: 2025-3-22 10:34 作者: BADGE 時間: 2025-3-22 16:55 作者: 尖牙 時間: 2025-3-22 19:12
Spatial Data Analysis Using Gaussian Markov Random Fields and Gaussian Processesyze spatial data.?It has a wide range of application and can be applied to one-dimensional data, two-dimensional grid data type, and geospatial map data. Later, we will see how a Gaussian process (GP) can be considered as a generalization of a GMRF. A GP can represent smooth functions, and usually gives high prediction performance.作者: 有組織 時間: 2025-3-23 00:45
Usages of MCMC Samples from Posterior and Predictive Distributionsons and predictive distributions were only kept on very basic levels, such as computing the intervals and visualizations. In this chapter, we will introduce more advanced usages of the?MCMC sample. They would be helpful in practice because it is very common to encounter the situation where we need to extract more information from the?MCMC sample.作者: Bumble 時間: 2025-3-23 05:02
https://doi.org/10.1007/978-981-19-4755-1Stan; Bayesian Modeling; Statistical Modeling; R; RStan; Python作者: crumble 時間: 2025-3-23 05:56 作者: 哎呦 時間: 2025-3-23 12:06
Human Health and the Environmentle to build your own model. We will introduce the processes to get ready for data analysis. Some of these processes will be the parts of a workflow in the following sections in this book. We also introduce the recommended statistical modeling workflow adopted in this book. From this section, the rea作者: COWER 時間: 2025-3-23 15:39 作者: Instinctive 時間: 2025-3-23 21:16
Antiviral Drugs Against Alphaherpesviruse future.?For an extrapolation problem, capturing mechanisms using a model which gives persuasive interpretations, usually yields a better prediction performance than using a black box method. In this chapter, we will use state space models for time series data. State space models are known for its 作者: maverick 時間: 2025-3-24 00:15
Vaccine Development for Cytomegalovirusyze spatial data.?It has a wide range of application and can be applied to one-dimensional data, two-dimensional grid data type, and geospatial map data. Later, we will see how a Gaussian process (GP) can be considered as a generalization of a GMRF. A GP can represent smooth functions, and usually g作者: conscience 時間: 2025-3-24 04:09 作者: Receive 時間: 2025-3-24 06:38 作者: Introduction 時間: 2025-3-24 12:10 作者: 草率男 時間: 2025-3-24 15:22
Insect-Borne Helminthiases: Filariases,We will introduce how we typically use Stan with the example of univariate regressions.?We will use R or Python to run Stan codes and estimate parameters. We will explain in detail how to do estimation, and how to use the draws?generated from MCMC, such as computing Bayesian confidence intervals and Bayesian prediction intervals.作者: overrule 時間: 2025-3-24 19:39
,Conclusions — a Geomedical View,We introduce regression models that are widely used, including multivariate regression, binomial logistic regression, logistic regression, and Poisson regression. Further, we discuss how to use visualization to check whether a model is proper.作者: TOXIN 時間: 2025-3-25 02:22
Human T-Cell Development in SCID-hu MiceThis chapter introduces several probability distributions that are commonly used in statistical modeling.?We explain the basic properties of these fundamental distributions, and further provide some examples to explain how to use them in practice, and other information that should be taken into account when building a model with them. 作者: 不再流行 時間: 2025-3-25 07:24
Human Humoral Immunity in SCID MiceWe will discuss several points that can potentially be problematic in extending regression analysis, and how to deal with these issues.作者: 獨特性 時間: 2025-3-25 10:40 作者: 熱心助人 時間: 2025-3-25 12:57 作者: CAJ 時間: 2025-3-25 19:24
Overview of StanWe will introduce probabilistic programming languages and Stan. We will explain the basic grammar of Stan, with the focuses on block structures, probabilistic generation, and for loop statement. We also give suggestions on the coding styles.作者: 配置 時間: 2025-3-25 22:34 作者: 直覺沒有 時間: 2025-3-26 02:14 作者: 稱贊 時間: 2025-3-26 07:03
Introduction of Probability DistributionsThis chapter introduces several probability distributions that are commonly used in statistical modeling.?We explain the basic properties of these fundamental distributions, and further provide some examples to explain how to use them in practice, and other information that should be taken into account when building a model with them. 作者: incubus 時間: 2025-3-26 11:50
Issues of RegressionWe will discuss several points that can potentially be problematic in extending regression analysis, and how to deal with these issues.作者: Memorial 時間: 2025-3-26 14:10
Other Advanced TopicsWe will introduce some advanced topics that have not been mentioned in the previous chapters.作者: 黑豹 時間: 2025-3-26 17:48
Book 2022 advanced topics for real-world data: longitudinal data analysis, state space models, spatial data analysis, Gaussian processes, Bayesian optimization, dimensionality reduction, model selection, and information criteria, demonstrating that Stan can solve any one of these problems in as little as 30 作者: 充滿人 時間: 2025-3-26 21:54
ial data analysis, Gaussian processes, Bayesian optimization, dimensionality reduction, model selection, and information criteria, demonstrating that Stan can solve any one of these problems in as little as 30 978-981-19-4757-5978-981-19-4755-1作者: 口味 時間: 2025-3-27 01:33
Bayesian Statistical Modeling with Stan, R, and Python作者: 清澈 時間: 2025-3-27 07:20
Bayesian Statistical Modeling with Stan, R, and Python978-981-19-4755-1作者: 牙齒 時間: 2025-3-27 13:09
Book 2022mming language..The book is divided into four parts. The first part reviews the theoretical background of modeling and Bayesian inference and presents a modeling workflow that makes modeling more engineering than art. The second part discusses the use of Stan, CmdStanR, and CmdStanPy from the very b作者: landmark 時間: 2025-3-27 15:54
astering modeling, including hierarchical models.Presents fu.This book provides a highly practical introduction to Bayesian statistical modeling with Stan, which has become the most popular probabilistic programming language..The book is divided into four parts. The first part reviews the theoretica作者: 自戀 時間: 2025-3-27 18:40
Human Health and the Environment the following sections in this book. We also introduce the recommended statistical modeling workflow adopted in this book. From this section, the readers might find that the statistical modeling is more similar to engineering than arts.作者: 輕快來事 時間: 2025-3-28 01:40 作者: 不能和解 時間: 2025-3-28 04:43 作者: 攤位 時間: 2025-3-28 07:29
Time Series Data Analysis with State Space Modelperformance than using a black box method. In this chapter, we will use state space models for time series data. State space models are known for its high interpretability, and because it can be extended easily, they have a wide range of applications. 作者: 脫水 時間: 2025-3-28 11:43 作者: farewell 時間: 2025-3-28 16:42 作者: 表狀態(tài) 時間: 2025-3-28 20:31 作者: 方舟 時間: 2025-3-29 02:15
Spatial Data Analysis Using Gaussian Markov Random Fields and Gaussian Processesyze spatial data.?It has a wide range of application and can be applied to one-dimensional data, two-dimensional grid data type, and geospatial map data. Later, we will see how a Gaussian process (GP) can be considered as a generalization of a GMRF. A GP can represent smooth functions, and usually g作者: Jubilation 時間: 2025-3-29 06:58 作者: 冰河期 時間: 2025-3-29 07:19
A Multiple Model Probability Hypothesis Density Tracker for Time-Lapse Cell Microscopy Sequencestion and introduce a general framework for particle tracking using the proposed filter. We apply our scheme to multi-target tracking in total internal reflection fluorescence microscopy (TIRFM) sequences and evaluate the performance of our filter against the existing LGJMS-PHD and IMM-JPDA filters.作者: 空中 時間: 2025-3-29 14:21 作者: 是貪求 時間: 2025-3-29 16:42 作者: 珍奇 時間: 2025-3-29 19:52 作者: alliance 時間: 2025-3-30 02:42 作者: 奇怪 時間: 2025-3-30 04:33 作者: 等待 時間: 2025-3-30 09:24
1383-5130 biodiversity conservation worldwide.Proposes environmental mThis book aims to cover the multitude of corporate approaches towards mainstreaming biodiversity conservation and ecological management in policies and action plans, and explores the roles of these efforts in achieving national and global t
