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- Nick Whiteley, Ali Taylan Cemgil, Simon J. Godsill
- ISMIR
- 2006

This paper presents a probabilistic model of temporal structure in music which allows joint inference of tempo, meter and rhythmic pattern. The framework of the model naturally quantifies these three musical concepts in terms of hidden state-variables, allowing resolution of otherwise apparent ambiguities in musical structure. At the heart of the system is… (More)

Switching state-space models (SSSM) are a popular class of time series models that have found many applications in statistics, econometrics and advanced signal processing. Bayesian inference for these models typically relies on Markov chain Monte Carlo (MCMC) techniques. However, even sophisticated MCMC methods dedicated to SSSM can prove quite ine cient as… (More)

- N. Whiteley, S. Singh, S. Godsill
- 2007 5th International Symposium on Image and…
- 2007

Optimal Bayesian multi-target filtering is, in general, computationally impractical due to the high dimensionality of the multi-target state. Recently Mahler, [9], introduced a filter which propagates the first moment of the multi-target posterior distribution, which he called the Probability Hypothesis Density (PHD) filter. While this reduces the… (More)

- Marcelo Pereyra, Nick Whiteley, Christophe Andrieu, Jean-Yves Tourneret
- 2014 IEEE Workshop on Statistical Signal…
- 2014

This paper addresses the problem of estimating the Potts-Markov random field parameter β jointly with the unknown parameters of a Bayesian image segmentation model. We propose a new adaptive Markov chain Monte Carlo (MCMC) algorithm for performing joint maximum marginal likelihood estimation of β and maximum-a-posteriori unsupervised image… (More)

In a recent paper [3], the Sequential Monte Carlo (SMC) sampler introduced in [12, 19, 24] has been shown to be asymptotically stable in the dimension of the state space d at a cost that is only polynomial in d, when N the number of Monte Carlo samples, is fixed. More precisely, it has been established that the effective sample size (ESS) of the ensuing… (More)

We introduce a general form of sequential Monte Carlo algorithm defined in terms of a parameterized resampling mechanism. We find that a suitably generalized notion of the Effective Sample Size (ESS), widely used to monitor algorithm degeneracy, appears naturally in a study of its convergence properties. We are then able to phrase sufficient conditions for… (More)

- Nick Whiteley
- 2011

This paper addresses nite sample stability properties of sequential Monte Carlo methods for approximating sequences of probability distributions. The results presented herein are applicable in the scenario where the start and end distributions in the sequence are xed and the number of intermediate steps is a parameter of the algorithm. Under assumptions… (More)

- Nick Whiteley, Adam M. Johansenand, Simon Godsill
- 2008

We present efficient Monte Carlo algorithms for performing Bayesian inference in a broad class of models: those in which the distributions of interest may be represented by time marginals of continuous-time jump processes conditional on a realisation of some noisy observation sequence. The sequential nature of the proposed algorithm makes it particularly… (More)

- Anthony Lee, Nick Whiteley
- Statistical Analysis and Data Mining
- 2016

This paper brings explicit considerations of distributed computing architectures and data structures into the rigorous design of Sequential Monte Carlo (SMC) methods. A theoretical result established recently by the authors shows that adapting interaction between particles to suitably control the Effective Sample Size (ESS) is sufficient to guarantee… (More)

- Cian O'Donnell, J. Tiago Gonçalves, Nick Whiteley, Carlos Portera-Cailliau, Terrence J. Sejnowski
- Neural Computation
- 2017

Our understanding of neural population coding has been limited by a lack of analysis methods to characterize spiking data from large populations. The biggest challenge comes from the fact that the number of possible network activity patterns scales exponentially with the number of neurons recorded ([Formula: see text]). Here we introduce a new statistical… (More)