
Subset Multivariate Collective And Point Anomaly Detection
In recent years, there has been a growing interest in identifying anomal...
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Stochastic Gradient MCMC for Nonlinear State Space Models
State space models (SSMs) provide a flexible framework for modeling comp...
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Stochastic gradient Markov chain Monte Carlo
Markov chain Monte Carlo (MCMC) algorithms are generally regarded as the...
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Control Variates for Stochastic Gradient MCMC
It is well known that Markov chain Monte Carlo (MCMC) methods scale poor...
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A loglinear time algorithm for constrained changepoint detection
Changepoint detection is a central problem in time series and genomic da...
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Detecting changes in slope with an L_0 penalty
Whilst there are many approaches to detecting changes in mean for a univ...
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Changepoint Detection in the Presence of Outliers
Many traditional methods for identifying changepoints can struggle in th...
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On the Identification and Mitigation of Weaknesses in the Knowledge Gradient Policy for MultiArmed Bandits
The Knowledge Gradient (KG) policy was originally proposed for online ra...
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Particle Metropolisadjusted Langevin algorithms
This paper proposes a new sampling scheme based on Langevin dynamics tha...
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Efficient penalty search for multiple changepoint problems
In the multiple changepoint setting, various search methods have been pr...
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Particle Metropolis adjusted Langevin algorithms for state space models
Particle MCMC is a class of algorithms that can be used to analyse state...
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Particle approximations of the score and observed information matrix for parameter estimation in state space models with linear computational cost
Poyiadjis et al. (2011) show how particle methods can be used to estimat...
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Efficient Bayesian analysis of multiple changepoint models with dependence across segments
We consider Bayesian analysis of a class of multiple changepoint models....
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Particle Filters and Data Assimilation
Statespace models can be used to incorporate subject knowledge on the u...
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sgmcmc: An R Package for Stochastic Gradient Markov Chain Monte Carlo
This paper introduces the R package sgmcmc; which can be used for Bayesi...
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Fast Nonconvex Deconvolution of Calcium Imaging Data
Calcium imaging data promises to transform the field of neuroscience by ...
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Continioustime Importance Sampling: Monte Carlo Methods which Avoid Timediscretisation Error
In this paper we develop a continuoustime sequential importance samplin...
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A linear time method for the detection of point and collective anomalies
The challenge of efficiently identifying anomalies in data sequences is ...
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Motor Unit Number Estimation via Sequential Monte Carlo
A change in the number of motor units that operate a particular muscle i...
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LargeScale Stochastic Sampling from the Probability Simplex
Stochastic gradient Markov chain Monte Carlo (SGMCMC) has become a popul...
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Generalized Functional Pruning Optimal Partitioning (GFPOP) for Constrained Changepoint Detection in Genomic Data
We describe a new algorithm and R package for peak detection in genomic ...
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Testing for a Change in Mean After Changepoint Detection
While many methods are available to detect structural changes in a time ...
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Consistency of a range of penalised cost approaches for detecting multiple changepoints
A common approach to detect multiple changepoints is to minimise a measu...
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Evaluation of extremal properties of GARCH(p,q) processes
Generalized autoregressive conditionally heteroskedastic (GARCH) process...
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Semiautomated simultaneous predictor selection for RegressionSARIMA models
Deciding which predictors to use plays an integral role in deriving stat...
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Detecting Abrupt Changes in the Presence of Local Fluctuations and Autocorrelated Noise
Whilst there are a plethora of algorithms for detecting changes in mean ...
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gfpop: an R Package for Univariate GraphConstrained Changepoint Detection
In a world with data that change rapidly and abruptly, it is important t...
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