5,532 research outputs found
Sequential Bayesian inference for implicit hidden Markov models and current limitations
Hidden Markov models can describe time series arising in various fields of
science, by treating the data as noisy measurements of an arbitrarily complex
Markov process. Sequential Monte Carlo (SMC) methods have become standard tools
to estimate the hidden Markov process given the observations and a fixed
parameter value. We review some of the recent developments allowing the
inclusion of parameter uncertainty as well as model uncertainty. The
shortcomings of the currently available methodology are emphasised from an
algorithmic complexity perspective. The statistical objects of interest for
time series analysis are illustrated on a toy "Lotka-Volterra" model used in
population ecology. Some open challenges are discussed regarding the
scalability of the reviewed methodology to longer time series,
higher-dimensional state spaces and more flexible models.Comment: Review article written for ESAIM: proceedings and surveys. 25 pages,
10 figure
Free energy Sequential Monte Carlo, application to mixture modelling
We introduce a new class of Sequential Monte Carlo (SMC) methods, which we
call free energy SMC. This class is inspired by free energy methods, which
originate from Physics, and where one samples from a biased distribution such
that a given function of the state is forced to be
uniformly distributed over a given interval. From an initial sequence of
distributions of interest, and a particular choice of ,
a free energy SMC sampler computes sequentially a sequence of biased
distributions with the following properties: (a) the
marginal distribution of with respect to is
approximatively uniform over a specified interval, and (b)
and have the same conditional distribution with respect to . We
apply our methodology to mixture posterior distributions, which are highly
multimodal. In the mixture context, forcing certain hyper-parameters to higher
values greatly faciliates mode swapping, and makes it possible to recover a
symetric output. We illustrate our approach with univariate and bivariate
Gaussian mixtures and two real-world datasets.Comment: presented at "Bayesian Statistics 9" (Valencia meetings, 4-8 June
2010, Benidorm
Gains in Power from Structured Two-Sample Tests of Means on Graphs
We consider multivariate two-sample tests of means, where the location shift
between the two populations is expected to be related to a known graph
structure. An important application of such tests is the detection of
differentially expressed genes between two patient populations, as shifts in
expression levels are expected to be coherent with the structure of graphs
reflecting gene properties such as biological process, molecular function,
regulation, or metabolism. For a fixed graph of interest, we demonstrate that
accounting for graph structure can yield more powerful tests under the
assumption of smooth distribution shift on the graph. We also investigate the
identification of non-homogeneous subgraphs of a given large graph, which poses
both computational and multiple testing problems. The relevance and benefits of
the proposed approach are illustrated on synthetic data and on breast cancer
gene expression data analyzed in context of KEGG pathways
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