https://nova.newcastle.edu.au/vital/access/manager/Index ${session.getAttribute("locale")} 5 Bayesian methods in meta-analysis https://nova.newcastle.edu.au/vital/access/manager/Repository/uon:2867 Wed 24 Jul 2013 22:53:24 AEST ]]> Computational Bayesian methods for communications and control https://nova.newcastle.edu.au/vital/access/manager/Repository/uon:12921 Wed 11 Apr 2018 17:03:37 AEST ]]> Using Bayesian frameworks to explore simple cognition https://nova.newcastle.edu.au/vital/access/manager/Repository/uon:21986 Wed 11 Apr 2018 15:10:20 AEST ]]> Systems theory and improving healthcare https://nova.newcastle.edu.au/vital/access/manager/Repository/uon:15307 Wed 11 Apr 2018 13:47:21 AEST ]]> Monitoring clinical indicators https://nova.newcastle.edu.au/vital/access/manager/Repository/uon:8786 Wed 11 Apr 2018 12:41:11 AEST ]]> Estimating uncertainty in maximum pit depth from limited observational data https://nova.newcastle.edu.au/vital/access/manager/Repository/uon:11521 Wed 11 Apr 2018 10:58:21 AEST ]]> Meta-analysis adjusting for heterogeneity, dependence and non-normality: a Bayesian parametric approach https://nova.newcastle.edu.au/vital/access/manager/Repository/uon:19277 Wed 11 Apr 2018 10:14:34 AEST ]]> Bayesian methods in reporting and managing Australian clinical indicators https://nova.newcastle.edu.au/vital/access/manager/Repository/uon:21010 th centile. The results are used to provide a relative measure to help prioritise quality improvement activity within clinical areas, rather than simply focus on "poorer performing" HCOs. The method draws attention to clinical areas exhibiting larger between-HCO variation and affecting larger numbers of patients. HCOs report data in six-month periods, resulting in estimated clinical indicator proportions which may be affected by small samples and sampling variation. Failing to address such issues would result in HCOs exhibiting extremely small and large estimated proportions and inflated estimates of the potential gains in the system. This paper describes the 20th centile method of calculating potential gains for the healthcare system by using Bayesian hierarchical models and shrinkage estimators to correct for the effects of sampling variation, and provides an example case in Emergency Medicine as well as example expert commentary from colleges based upon the reports. The application of these Bayesian methods enables all collated data to be used, irrespective of an HCO's size, and facilitates more realistic estimates of potential system gains.]]> Wed 11 Apr 2018 09:32:32 AEST ]]> Bayesian analyses of cognitive architecture https://nova.newcastle.edu.au/vital/access/manager/Repository/uon:31505 Tue 27 Mar 2018 18:00:32 AEDT ]]> A Bayesian analysis of a regime switching volatility model https://nova.newcastle.edu.au/vital/access/manager/Repository/uon:28972 Thu 26 Jul 2018 16:02:23 AEST ]]> Adjusting for differential sampling in a Bayesian multivariate meta-analysis of repeated measures data https://nova.newcastle.edu.au/vital/access/manager/Repository/uon:2856 Sat 24 Mar 2018 08:28:57 AEDT ]]> Context effects in multi-alternative decision making: empirical data and a Bayesian model https://nova.newcastle.edu.au/vital/access/manager/Repository/uon:16199 Sat 24 Mar 2018 08:03:05 AEDT ]]> Finding the optimal statistical model to describe target motion during radiotherapy delivery - a Bayesian approach https://nova.newcastle.edu.au/vital/access/manager/Repository/uon:21551 Sat 24 Mar 2018 07:50:23 AEDT ]]> Asymmetric response and interaction of US and local news in financial markets https://nova.newcastle.edu.au/vital/access/manager/Repository/uon:133 Sat 24 Mar 2018 07:43:14 AEDT ]]> Individualised medicine: why we need Bayesian dosing https://nova.newcastle.edu.au/vital/access/manager/Repository/uon:30694 Mon 23 Sep 2019 13:58:52 AEST ]]> Bayesian estimation and model selection of a multivariate smooth transition autoregressive model https://nova.newcastle.edu.au/vital/access/manager/Repository/uon:43051 k (M-STAR)(k) is a nonlinear multivariate time series model able to capture regime changes in the conditional mean. The main aim of this paper is to develop a Bayesian estimation scheme for the M-STAR(k) model that includes the coefficient parameter matrix, transition function parameters, covariance parameter matrix, and the model order k as parameters to estimate. To achieve this aim, the joint posterior distribution of the parameters for the M-STAR(k) model is derived. The conditional posterior distributions are then shown, followed by the design of a posterior simulator using a combination of Markov chain Monte Carlo (MCMC) algorithms that includes the Metropolis-Hastings, Gibbs sampler, and reversible jump MCMC algorithms. Following this, extensive simulation studies, as well as case studies, are detailed at the end.]]> Mon 12 Sep 2022 14:16:24 AEST ]]> Use in practice of importance sampling for repeated MCMC for Poisson models https://nova.newcastle.edu.au/vital/access/manager/Repository/uon:11121 Mon 09 Sep 2019 12:53:18 AEST ]]> Bayesian Federated Learning: A Survey https://nova.newcastle.edu.au/vital/access/manager/Repository/uon:53179 Fri 17 Nov 2023 11:37:08 AEDT ]]>