Search Results for author:
Found 30 papers, 18 papers with code
A Constant-per-Iteration Likelihood Ratio Test for Online Changepoint Detection for Exponential Family Models
This is possible by using pruning ideas, which reduce the set of changepoint locations that need to be considered at time $T$ to approximately $\log T$.
A Log-Linear Non-Parametric Online Changepoint Detection Algorithm based on Functional Pruning
Thanks to functional pruning ideas, NP-FOCuS has a computational cost that is log-linear in the number of observations and is suitable for high-frequency data streams.
Automatic Change-Point Detection in Time Series via Deep Learning
We show how to automatically generate new offline detection methods based on training a neural network.
Preferential Subsampling for Stochastic Gradient Langevin Dynamics
Stochastic gradient MCMC (SGMCMC) offers a scalable alternative to traditional MCMC, by constructing an unbiased estimate of the gradient of the log-posterior with a small, uniformly-weighted subsample of the data.
Continuously-Tempered PDMP Samplers
We show how PDMPs, and particularly the Zig-Zag sampler, can be implemented to sample from such an extended distribution.
Efficient computation of the volume of a polytope in high-dimensions using Piecewise Deterministic Markov Processes
Computing the volume of a polytope in high dimensions is computationally challenging but has wide applications.
Fast Online Changepoint Detection via Functional Pruning CUSUM statistics
Online algorithms for detecting a change in mean often involve using a moving window, or specifying the expected size of change.
Reversible Jump PDMP Samplers for Variable Selection
A new class of Markov chain Monte Carlo (MCMC) algorithms, based on simulating piecewise deterministic Markov processes (PDMPs), have recently shown great promise: they are non-reversible, can mix better than standard MCMC algorithms, and can use subsampling ideas to speed up computation in big data scenarios.
Subset Multivariate Collective And Point Anomaly Detection
In recent years, there has been a growing interest in identifying anomalous structure within multivariate data streams.
Stochastic gradient Markov chain Monte Carlo
In this paper, we focus on a particular class of scalable Monte Carlo algorithms, stochastic gradient Markov chain Monte Carlo (SGMCMC) which utilises data subsampling techniques to reduce the per-iteration cost of MCMC.
Stochastic Gradient MCMC for Nonlinear State Space Models
We evaluate our proposed particle buffered stochastic gradient using stochastic gradient MCMC for inference on both long sequential synthetic and minute-resolution financial returns data, demonstrating the importance of this class of methods.
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 data sets using constrained changepoint algorithms.
Computation
Large-Scale Stochastic Sampling from the Probability Simplex
Unfortunately, many popular large-scale Bayesian models, such as network or topic models, require inference on sparse simplex spaces.
A linear time method for the detection of point and collective anomalies
Theoretical results establish the consistency of CAPA at detecting collective anomalies and, as a by-product, the consistency of a popular penalised cost based change in mean and variance detection method.
Fast Nonconvex Deconvolution of Calcium Imaging Data
Calcium imaging data promises to transform the field of neuroscience by making it possible to record from large populations of neurons simultaneously.
Methodology Neurons and Cognition Applications
sgmcmc: An R Package for Stochastic Gradient Markov Chain Monte Carlo
To do this, the package uses the software library TensorFlow, which has a variety of statistical distributions and mathematical operations as standard, meaning a wide class of models can be built using this framework.
Control Variates for Stochastic Gradient MCMC
These methods use a noisy estimate of the gradient of the log posterior, which reduces the per iteration computational cost of the algorithm.
A log-linear time algorithm for constrained changepoint detection
This leads to a new algorithm which can solve problems with arbitrary affine constraints on adjacent segment means, and which has empirical time complexity that is log-linear in the amount of data.
Piecewise Deterministic Markov Processes for Scalable Monte Carlo on Restricted Domains
Piecewise Deterministic Monte Carlo algorithms enable simulation from a posterior distribution, whilst only needing to access a sub-sample of data at each iteration.
Methodology Computation
Detecting changes in slope with an $L_0$ penalty
We define best based on a criterion that measures fit to data using the residual sum of squares, but penalises complexity based on an $L_0$ penalty on changes in slope.
Piecewise Deterministic Markov Processes for Continuous-Time Monte Carlo
Recently there have been exciting developments in Monte Carlo methods, with the development of new MCMC and sequential Monte Carlo (SMC) algorithms which are based on continuous-time, rather than discrete-time, Markov processes.
Changepoint Detection in the Presence of Outliers
We present an approach to changepoint detection that is robust to the presence of outliers.
On the Identification and Mitigation of Weaknesses in the Knowledge Gradient Policy for Multi-Armed Bandits
We study its use in a class of exponential family MABs and identify weaknesses, including a propensity to take actions which are dominated with respect to both exploitation and exploration.
The Zig-Zag Process and Super-Efficient Sampling for Bayesian Analysis of Big Data
Standard MCMC methods can scale poorly to big data settings due to the need to evaluate the likelihood at each iteration.
Computation Probability 65C60, 65C05, 62F15, 60J25
Particle Metropolis-adjusted Langevin algorithms
This paper proposes a new sampling scheme based on Langevin dynamics that is applicable within pseudo-marginal and particle Markov chain Monte Carlo algorithms.
Efficient penalty search for multiple changepoint problems
The computational complexity of this approach can be linear in the number of data points and linear in the difference between the number of changepoints in the optimal segmentations for the smallest and largest penalty values.
Augmentation Schemes for Particle MCMC
We then use the MCMC moves to update the latent variables, and the particle filter to propose new values for the parameters and stochastic process given the latent variables.
Particle Metropolis adjusted Langevin algorithms for state space models
Currently the default is to use random walk Metropolis to update the parameter values.
Particle approximations of the score and observed information matrix for parameter estimation in state space models with linear computational cost
This paper introduces an alternative approach for estimating these terms at a computational cost that is linear in the number of particles.
Efficient Bayesian analysis of multiple changepoint models with dependence across segments
We consider Bayesian analysis of a class of multiple changepoint models.
