3,588 research outputs found
Particle Gibbs Split-Merge Sampling for Bayesian Inference in Mixture Models
This paper presents a new Markov chain Monte Carlo method to sample from the
posterior distribution of conjugate mixture models. This algorithm relies on a
flexible split-merge procedure built using the particle Gibbs sampler. Contrary
to available split-merge procedures, the resulting so-called Particle Gibbs
Split-Merge sampler does not require the computation of a complex acceptance
ratio, is simple to implement using existing sequential Monte Carlo libraries
and can be parallelized. We investigate its performance experimentally on
synthetic problems as well as on geolocation and cancer genomics data. In all
these examples, the particle Gibbs split-merge sampler outperforms
state-of-the-art split-merge methods by up to an order of magnitude for a fixed
computational complexity
Robust adaptive Metropolis algorithm with coerced acceptance rate
The adaptive Metropolis (AM) algorithm of Haario, Saksman and Tamminen
[Bernoulli 7 (2001) 223-242] uses the estimated covariance of the target
distribution in the proposal distribution. This paper introduces a new robust
adaptive Metropolis algorithm estimating the shape of the target distribution
and simultaneously coercing the acceptance rate. The adaptation rule is
computationally simple adding no extra cost compared with the AM algorithm. The
adaptation strategy can be seen as a multidimensional extension of the
previously proposed method adapting the scale of the proposal distribution in
order to attain a given acceptance rate. The empirical results show promising
behaviour of the new algorithm in an example with Student target distribution
having no finite second moment, where the AM covariance estimate is unstable.
In the examples with finite second moments, the performance of the new approach
seems to be competitive with the AM algorithm combined with scale adaptation.Comment: 21 pages, 3 figure
Recent advances in directional statistics
Mainstream statistical methodology is generally applicable to data observed
in Euclidean space. There are, however, numerous contexts of considerable
scientific interest in which the natural supports for the data under
consideration are Riemannian manifolds like the unit circle, torus, sphere and
their extensions. Typically, such data can be represented using one or more
directions, and directional statistics is the branch of statistics that deals
with their analysis. In this paper we provide a review of the many recent
developments in the field since the publication of Mardia and Jupp (1999),
still the most comprehensive text on directional statistics. Many of those
developments have been stimulated by interesting applications in fields as
diverse as astronomy, medicine, genetics, neurology, aeronautics, acoustics,
image analysis, text mining, environmetrics, and machine learning. We begin by
considering developments for the exploratory analysis of directional data
before progressing to distributional models, general approaches to inference,
hypothesis testing, regression, nonparametric curve estimation, methods for
dimension reduction, classification and clustering, and the modelling of time
series, spatial and spatio-temporal data. An overview of currently available
software for analysing directional data is also provided, and potential future
developments discussed.Comment: 61 page
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