Bayes linear kinematics in a dynamic survival model
نویسندگان
چکیده
منابع مشابه
Local Computation Aspects of Bayes Linear Kinematics
Goldstein and Shaw (2002) developed Bayes linear kinematics describing how a Bayes linear analysis should be carried out when we only receive partial information which changes our beliefs about a vector of random quantities in some generalised way. In this paper we explore principles of local computation for Bayes linear kinematic updates. This theory is illustrated by the Bayes linear Bayes mo...
متن کاملBayes Linear Covariance Matrix Adjustment for Multivariate Dynamic Linear Models
A methodology is developed for the Bayes linear adjustment of the covariance matrices underlying a multivariate constant time series dynamic linear model. The covariance matrices are embedded in a distribution-free inner-product space of matrix objects which facilitates such adjustment. This approach helps to make the analysis simple, tractable and robust. To illustrate the methods, a simple mo...
متن کاملBayes Estimation in Meta-analysis using a linear model theorem
A Hierarchical Bayesian meta-analysis model developed by Dumouchel is derived by implementing the General Bayesian Linear model (GBLM) theorem. The aim is to obtain the joint posterior distribution of all parameters in the model. Simulation study is conducted to confirm the estimation of all parameters of interest. Results show parameter estimates as close to the true values indicating paramete...
متن کاملLinear Empirical Bayes Estimation of Survival Probabilities with Partial Data
In this paper we consider linear empirical Bayes estimation of survival probabilities with partial data from right-censored and possibly left-truncated observations. Such data are produced by studies in which the exact times of death are not recorded and the length of time that each subject may be under observation cannot exceed one unit of time. We obtain asymptotically optimal linear empirica...
متن کاملConstructing a Dynamic Bayes Net Model of Academic Advising
In this paper we apply ideas from collaborative filtering to the problem of building dynamic Bayesian network (DBN) models for planning. We demonstrate that item-based collaborative filtering can be used to construct dynamic Bayesian networks for use in large, factored domains with sparse data. Such Bayesian networks can model the transition function for decision-theoretic planning. We demonstr...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: International Journal of Approximate Reasoning
سال: 2017
ISSN: 0888-613X
DOI: 10.1016/j.ijar.2016.09.010