23,522 research outputs found
RMCMC: A System for Updating Bayesian Models
A system to update estimates from a sequence of probability distributions is
presented. The aim of the system is to quickly produce estimates with a
user-specified bound on the Monte Carlo error. The estimates are based upon
weighted samples stored in a database. The stored samples are maintained such
that the accuracy of the estimates and quality of the samples is satisfactory.
This maintenance involves varying the number of samples in the database and
updating their weights. New samples are generated, when required, by a Markov
chain Monte Carlo algorithm. The system is demonstrated using a football league
model that is used to predict the end of season table. Correctness of the
estimates and their accuracy is shown in a simulation using a linear Gaussian
model
The chopthin algorithm for resampling
Resampling is a standard step in particle filters and more generally
sequential Monte Carlo methods. We present an algorithm, called chopthin, for
resampling weighted particles. In contrast to standard resampling methods the
algorithm does not produce a set of equally weighted particles; instead it
merely enforces an upper bound on the ratio between the weights. Simulation
studies show that the chopthin algorithm consistently outperforms standard
resampling methods. The algorithms chops up particles with large weight and
thins out particles with low weight, hence its name. It implicitly guarantees a
lower bound on the effective sample size. The algorithm can be implemented
efficiently, making it practically useful. We show that the expected
computational effort is linear in the number of particles. Implementations for
C++, R (on CRAN), Python and Matlab are available.Comment: 14 pages, 4 figure
Exploring the challenges of implementing e-health: a protocol for an update of a systematic review of reviews.
There is great potential for e-health to deliver cost-effective, quality healthcare and spending on e-health systems by governments and healthcare systems is increasing worldwide. However, the literature often describes problematic and unsuccessful attempts to implement these new technologies into routine clinical practice. To understand and address the challenges of implementing e-health, a systematic review was conducted in 2009, which identified several conceptual barriers and facilitators to implementation. As technology is rapidly changing and new e-health solutions are constantly evolving to meet the needs of current practice, an update of this review is deemed necessary to understand current challenges to the implementation of e-health. This research aims to identify, summarise and synthesise currently available evidence, by undertaking a systematic review of reviews to explore the barriers and facilitators to implementing e-health across a range of healthcare settings
Bayesian nonparametric estimation and consistency of mixed multinomial logit choice models
This paper develops nonparametric estimation for discrete choice models based
on the mixed multinomial logit (MMNL) model. It has been shown that MMNL models
encompass all discrete choice models derived under the assumption of random
utility maximization, subject to the identification of an unknown distribution
. Noting the mixture model description of the MMNL, we employ a Bayesian
nonparametric approach, using nonparametric priors on the unknown mixing
distribution , to estimate choice probabilities. We provide an important
theoretical support for the use of the proposed methodology by investigating
consistency of the posterior distribution for a general nonparametric prior on
the mixing distribution. Consistency is defined according to an -type
distance on the space of choice probabilities and is achieved by extending to a
regression model framework a recent approach to strong consistency based on the
summability of square roots of prior probabilities. Moving to estimation,
slightly different techniques for non-panel and panel data models are
discussed. For practical implementation, we describe efficient and relatively
easy-to-use blocked Gibbs sampling procedures. These procedures are based on
approximations of the random probability measure by classes of finite
stick-breaking processes. A simulation study is also performed to investigate
the performance of the proposed methods.Comment: Published in at http://dx.doi.org/10.3150/09-BEJ233 the Bernoulli
(http://isi.cbs.nl/bernoulli/) by the International Statistical
Institute/Bernoulli Society (http://isi.cbs.nl/BS/bshome.htm
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