Reference EntryDOI
Bayesian Survival Analysis
TLDR
This paper reviewed parametric and semiparametric approaches to Bayesian survival analysis, with a focus on proportional hazards models, and reference to other types of models are also given, including Gibbs sampling and Weibull model.Abstract:
Great strides in the analysis of survival data using Bayesian methods have been made in the past ten years due to advances in Bayesian computation and the feasibility of such methods. In this chapter, we review Bayesian advances in survival analysis and discuss the various semiparametric modeling techniques that are now commonly used. We review parametric and semiparametric approaches to Bayesian survival analysis, with a focus on proportional hazards models. Reference to other types of models are also given.
Keywords:
beta process;
Cox model;
Dirichlet process;
gamma process;
Gibbs sampling;
piecewise exponential model;
Weibull modelread more
Citations
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Journal ArticleDOI
Risk of recurrence of gastrointestinal stromal tumour after surgery: an analysis of pooled population-based cohorts
Heikki Joensuu,Aki Vehtari,Jaakko Riihimäki,Toshirou Nishida,Sonja E. Steigen,Peter Brabec,Lukáš Plank,Bengt Nilsson,Claudia Cirilli,Chiara Braconi,Andrea Bordoni,Magnus K. Magnusson,Zdenek Linke,Jozef Sufliarsky,Massimo Federico,Jon G. Jonasson,Angelo Paolo Dei Tos,Piotr Rutkowski +17 more
TL;DR: Although the modified NIH classification is the best criteria to identify a single high-risk group for consideration of adjuvant therapy, the prognostic contour maps resulting from non-linear modelling are appropriate for estimation of individualised outcomes.
Proceedings ArticleDOI
DRN: A Deep Reinforcement Learning Framework for News Recommendation
TL;DR: A Deep Q-Learning based recommendation framework, which can model future reward explicitly, is proposed, which considers user return pattern as a supplement to click / no click label in order to capture more user feedback information.
Journal ArticleDOI
A general framework for updating belief distributions.
TL;DR: It is argued that a valid update of a prior belief distribution to a posterior can be made for parameters which are connected to observations through a loss function rather than the traditional likelihood function, which is recovered as a special case.
Journal ArticleDOI
Basic Concepts and Methods for Joint Models of Longitudinal and Survival Data
TL;DR: An introductory overview on joint modeling for longitudinal and survival data is given and a general discussion of a broad range of issues that arise in the design and analysis of clinical trials using joint models are presented.
Handbook Of Data Mining
Nong Ye,Oded Maimon +1 more
TL;DR: The Handbook of Data Science as mentioned in this paper is a popular textbook for statistical analysis and data mining applications, which won the PROSE award for top Mathematics book in 2009 and has been used extensively in the field of data visualization.
References
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Journal ArticleDOI
A Bayesian Analysis of Some Nonparametric Problems
TL;DR: In this article, a class of prior distributions, called Dirichlet process priors, is proposed for nonparametric problems, for which treatment of many non-parametric statistical problems may be carried out, yielding results that are comparable to the classical theory.
Journal ArticleDOI
Bayesian Density Estimation and Inference Using Mixtures
Michael Escobar,Mike West +1 more
TL;DR: In this article, the authors describe and illustrate Bayesian inference in models for density estimation using mixtures of Dirichlet processes and show convergence results for a general class of normal mixture models.
Journal ArticleDOI
Mixtures of Dirichlet Processes with Applications to Bayesian Nonparametric Problems
TL;DR: In this article, the conditional distribution of the random measure, given the observations, is no longer that of a simple Dirichlet process, but can be described as being a mixture of DirICHlet processes.
Journal ArticleDOI
Prior Distributions on Spaces of Probability Measures
TL;DR: In this paper, a review of methods of generating prior distributions on spaces of probability measures for use in Bayesian nonparametric inference is presented, with special emphasis on the Dirichlet processes, the tail free processes, and processes neutral to the right.
MonographDOI
Statistical Prediction Analysis
John Aitchison,I. R. Dunsmore +1 more
TL;DR: This paper presents a meta-modelling procedure that automates the very labor-intensive and therefore time-heavy and expensive and expensive process of manually cataloging and forecasting the distribution of distributions in a discrete-time manner.