Exact and efficient Bayesian inference for multiple changepoint problems

By Paul Fearnhead. Statistics and computing, 2006

519 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/5fe9451593780ebdd9261f9dcdaa3aa673d2bceb

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Citations in the corpus

Oboe: auto-tuning video ABR algorithms to network conditions (2018, methodology): “While several techniques exist for change point detection [22, 33, 36, 44, 54, 58], we focus on probabilistic methods [14, 18, 24, 57].”
A review of change point detection methods (2018, methodology): “The reader can nd more information in the literature, for instance on Bayesian methods [48, 100], an in-depth theoretical survey [72] and numerical comparisons in controlled settings [44].”
Markov Chain Monte Carlo Methods and the Label Switching Problem in Bayesian Mixture Modeling (2005, methodology): “Also work on exact simulation for change-point problems via the forward–backward method can be found in Fearnhead (2004).”
Explicit-Duration Markov Switching Models (2014, methodology): “More recent Bayesian approaches define a distribution on the number and positions of changepoints through a segmentduration distribution [Fearnhead, 2006, Fearnhead and Liu, 2007, Adams and MacKay, 2007, Fearnhead and Vasileiou, 2009, Eckley et al., 2011].”
Variational Inference for Gaussian Process Modulated Poisson Processes (2014, methodology): “Our inferred intensity of disasters correlates with the historical introduction of safety regulation, as noted in previous work on this data (Fearnhead, 2006; Carlin et al., 1992).”
Bayesian multivariate linear regression with application to change point models in hydrometeorological variables (2007, methodology): “Examples of alternative strategies for handling an unknown number of change points can also be found in work by Barry and Hartigan [1993], Fearnhead [2005] or Seidou and Ouarda [2006].”
Segmenting sign language into motor primitives with Bayesian binning (2013, background): “Concurrently, a closely related Bayesian formalism for dealing with multiple change point problems was introduced by Fearnhead (2006).”
Bayesian reasoning and machine learning (2012, unknown): “, S}, with a reset that cuts dependency of the continuous variable on the past[93, 57]: p(ht|ht−1, st, ct) = { p(ht|ht−1, st) ct = 0 p(1)(ht|st) ct = 1 (25.”
Quantifying the uncertainty in change points (2012, methodology): “This definition is currently adopted in existing works such as Chib (1998); Hamilton (1989); Durbin et al. (1998); Fearnhead (2006).”
Hierarchical Aligned Cluster Analysis for Temporal Clustering of Human Motion (2013, background): “The goal of change-point detection [32], [33], [34] is to identify changes at unknown times and estimate the location of changes in stochastic processes.”
On‐line inference for multiple changepoint problems (2007, methodology): “For examples of the potential difficulties with MCMC methods caused by (a), compare the inferences that were obtained for the coal-mining disaster data which were analysed in Green (1995) with those based on the direct simulation method of Fearnhead (2006).”
Inference for single and multiple change‐points in time series (2013, methodology): “…2000b), change in the mean vector of a multivariate normal distribution (Perreault et al., 2000c), single change in the parameters of a multiple LR model (Seidou et al., 2007) and also the more complex case of estimating multiple change-points (Fearnhead, 2005, 2006; Seidou and Ouarda, 2007).”
EXACT ASYMPTOTIC DISTRIBUTION OF CHANGE-POINT MLE FOR CHANGE IN THE MEAN OF GAUSSIAN SEQUENCES (2010, methodology): “For examples of real life applications, see Braun and Müller (1998) for application of change-point methods in DNA segmentation and bioinformatics; Fearnhead (2006), Ruggieri et al. (2009) for applications in geology; Perreault et al. (2000a, 2000b) for application in hydrology; Jarušková (1996)…”
Kernel-Based Methods for Hypothesis Testing: A Unified View (2013, background): “IEEE SIGNAL PROCESSING MAGAZINE [89] JULY 2013 approaches taking the signal as whole and typically scale quadratically in the length of the signals [6], [24].”
Homogeneity and change-point detection tests for multivariate data using rank statistics (2011, methodology): “In such situations, very convincing results have been demonstrated using Bayesian approaches due to the existence of efficient computational methods to explore the posterior distribution, even when using very flexible models (Barry and Hartigan, 1992; Fearnhead, 2006).”
Multiple Change-Point Estimation With a Total Variation Penalty (2010, methodology): “A computationally efficient way of solving this minimization is based on a dynamic programming algorithm (DP), originally proposed by Fisher (1958); Bellman (1961) and described in Kay (1993), chapter 12.”