Dirichlet Process Hidden Markov Multiple Change-point Model
By S. Ko, Terence Tai-Leung Chong, Pulak Ghosh. 2015
57 citations recorded by the source corpus.
This paper proposes a new Bayesian multiple change-point model which is based on the hidden Markov approach. The Dirichlet process hidden Markov model does not require the specification of the number of change-points a priori. Hence our model is robust to model specification in contrast to the fully parametric Bayesian model. We propose a general Markov chain Monte Carlo algorithm which only needs to sample the states around change-points. Simulations for a normal mean-shift model with known and unknown variance demonstrate advantages of our approach. Two applications, namely the coal-mining disaster data and the real US GDP growth, are provided. We detect a single change-point for both the disaster data and US GDP growth. All the change-point locations and posterior inferences of the two applications are in line with existing methods.
Subfields: Artificial Intelligence, Demography, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/fa290a87f37fa4ec610881bca246192d9d77e1ec
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
In Demography
In Statistics and Probability
Citations in the corpus
- A review of change point detection methods (2018, methodology): “Developed by [82] with strong connections to [36], Dirichlet process hidden Markov models are well adapted to perform change point detection.”
- Selective review of offline change point detection methods (2020, methodology): “123 This model was later extended, for instance with Dirichlet processes [39, 40] 124 or product partition models [41, 42].”