arXiv: Computation最新文献_第3页

Sequential quasi-Monte Carlo: Introduction for Non-Experts, Dimension Reduction, Application to Partly Observed Diffusion Processes 序贯拟蒙特卡罗:非专家介绍，降维，部分观测扩散过程的应用

arXiv: Computation Pub Date : 2017-06-16 DOI: 10.1007/978-3-319-91436-7_5

N. Chopin, Mathieu Gerber

引用次数: 3

Optimal Experimental Design Using A Consistent Bayesian Approach 使用一致贝叶斯方法的最优实验设计

arXiv: Computation Pub Date : 2017-05-25 DOI: 10.1115/1.4037457

Scott N. Walsh, T. Wildey, J. Jakeman

引用次数: 15

Parsimonious Adaptive Rejection Sampling 简约自适应拒绝抽样

arXiv: Computation Pub Date : 2017-05-01 DOI: 10.1049/EL.2017.1711

Luca Martino

引用次数: 10

Estimating Spatial Econometrics Models with Integrated Nested Laplace Approximation 利用积分嵌套拉普拉斯近似估计空间计量经济学模型

arXiv: Computation Pub Date : 2017-03-03 DOI: 10.3390/MATH9172044

V. Gómez‐Rubio, R. Bivand, H. Rue

引用次数: 26

Tutorial in Joint Modeling and Prediction: a Statistical Software for Correlated Longitudinal Outcomes, Recurrent Events and a Terminal Event 联合建模与预测教程:一种相关纵向结果、复发事件和终点事件的统计软件

arXiv: Computation Pub Date : 2017-01-13 DOI: 10.18637/jss.v081.i03

Agnieszka Kr'ol, A. Mauguen, Yassin Mazroui, Alexandre Laurent, S. Michiels, V. Rondeau

{"title":"Tutorial in Joint Modeling and Prediction: a Statistical Software for Correlated Longitudinal Outcomes, Recurrent Events and a Terminal Event","authors":"Agnieszka Kr'ol, A. Mauguen, Yassin Mazroui, Alexandre Laurent, S. Michiels, V. Rondeau","doi":"10.18637/jss.v081.i03","DOIUrl":"https://doi.org/10.18637/jss.v081.i03","url":null,"abstract":"Extensions in the field of joint modeling of correlated data and dynamic predictions improve the development of prognosis research. The R package frailtypack provides estimations of various joint models for longitudinal data and survival events. In particular, it fits models for recurrent events and a terminal event (frailtyPenal), models for two survival outcomes for clustered data (frailtyPenal), models for two types of recurrent events and a terminal event (multivPenal), models for a longitudinal biomarker and a terminal event (longiPenal) and models for a longitudinal biomarker, recurrent events and a terminal event (trivPenal). The estimators are obtained using a standard and penalized maximum likelihood approach, each model function allows to evaluate goodness-of-fit analyses and plots of baseline hazard functions. Finally, the package provides individual dynamic predictions of the terminal event and evaluation of predictive accuracy. This paper presents theoretical models with estimation techniques, applies the methods for predictions and illustrates frailtypack functions details with examples.","PeriodicalId":8446,"journal":{"name":"arXiv: Computation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85044450","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 50

Calculating probabilistic excursion sets and related quantities using excursions 使用漂移计算概率漂移集和相关数量

arXiv: Computation Pub Date : 2016-12-13 DOI: 10.18637/JSS.V086.I05

D. Bolin, F. Lindgren

引用次数: 29

ADDT : An R Package for Analysis of Accelerated Destructive Degradation Test Data 用于加速破坏性退化试验数据分析的R包

arXiv: Computation Pub Date : 2016-11-22 DOI: 10.1007/978-981-10-5194-4_14

Zhongnan Jin, Yimeng Xie, Yili Hong, J. V. Mullekom

引用次数: 5

Local Kernel Dimension Reduction in Approximate Bayesian Computation 近似贝叶斯计算中的局部核维降维

arXiv: Computation Pub Date : 2016-09-05 DOI: 10.4236/OJS.2018.83031

Jin Zhou, K. Fukumizu

{"title":"Local Kernel Dimension Reduction in Approximate Bayesian Computation","authors":"Jin Zhou, K. Fukumizu","doi":"10.4236/OJS.2018.83031","DOIUrl":"https://doi.org/10.4236/OJS.2018.83031","url":null,"abstract":"Approximate Bayesian Computation (ABC) is a popular sampling method in applications involving intractable likelihood functions. Without evaluating the likelihood function, ABC approximates the posterior distribution by the set of accepted samples which are simulated with parameters drawn from the prior distribution, where acceptance is determined by the distance between the summary statistics of the sample and the observation. The sufficiency and dimensionality of the summary statistics play a central role in the application of ABC. This paper proposes Local Gradient Kernel Dimension Reduction (LGKDR) to construct low dimensional summary statistics for ABC. The proposed method identifies a sufficient subspace of the original summary statistics by implicitly considers all nonlinear transforms therein, and a weighting kernel is used for the concentration of the projections. No strong assumptions are made on the marginal distributions nor the regression model, permitting usage in a wide range of applications. Experiments are done with both simple rejection ABC and sequential Monte Carlo ABC methods. Results are reported as competitive in the former and substantially better in the latter cases in which Monte Carlo errors are compressed as much as possible.","PeriodicalId":8446,"journal":{"name":"arXiv: Computation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77129459","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Fast Simulation of Hyperplane-Truncated Multivariate Normal Distributions 超平面截断多元正态分布的快速模拟

arXiv: Computation Pub Date : 2016-07-16 DOI: 10.1214/17-BA1052

Yulai Cong, Bo Chen, Mingyuan Zhou

引用次数: 36

Robust and scalable Bayesian analysis of spatial neural tuning function data 空间神经调谐函数数据的鲁棒和可扩展贝叶斯分析

arXiv: Computation Pub Date : 2016-06-24 DOI: 10.1214/16-AOAS996

Kamiar Rahnama Rad, Timothy A. Machado, L. Paninski

{"title":"Robust and scalable Bayesian analysis of spatial neural tuning function data","authors":"Kamiar Rahnama Rad, Timothy A. Machado, L. Paninski","doi":"10.1214/16-AOAS996","DOIUrl":"https://doi.org/10.1214/16-AOAS996","url":null,"abstract":"A common analytical problem in neuroscience is the interpretation of neural activity with respect to sensory input or behavioral output. This is typically achieved by regressing measured neural activity against known stimuli or behavioral variables to produce a \"tuning function\" for each neuron. Unfortunately, because this approach handles neurons individually, it cannot take advantage of simultaneous measurements from spatially adjacent neurons that often have similar tuning properties. On the other hand, sharing information between adjacent neurons can errantly degrade estimates of tuning functions across space if there are sharp discontinuities in tuning between nearby neurons. In this paper, we develop a computationally efficient block Gibbs sampler that effectively pools information between neurons to de-noise tuning function estimates while simultaneously preserving sharp discontinuities that might exist in the organization of tuning across space. This method is fully Bayesian and its computational cost per iteration scales sub-quadratically with total parameter dimensionality. We demonstrate the robustness and scalability of this approach by applying it to both real and synthetic datasets. In particular, an application to data from the spinal cord illustrates that the proposed methods can dramatically decrease the experimental time required to accurately estimate tuning functions.","PeriodicalId":8446,"journal":{"name":"arXiv: Computation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84636232","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4