Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Efficient Bayesian analysis of multiple changepoint models with
  dependence across segments   We consider Bayesian analysis of a class of multiple changepoint models.
While there are a variety of efficient ways to analyse these models if the
parameters associated with each segment are independent, there are few general
approaches for models where the parameters are dependent. Under the assumption
that the dependence is Markov, we propose an efficient online algorithm for
sampling from an approximation to the posterior distribution of the number and
position of the changepoints. In a simulation study, we show that the
approximation introduced is negligible. We illustrate the power of our approach
through fitting piecewise polynomial models to data, under a model which allows
for either continuity or discontinuity of the underlying curve at each
changepoint. This method is competitive with, or out-performs, other methods
for inferring curves from noisy data; and uniquely it allows for inference of
the locations of discontinuities in the underlying curve.

14
The categories of this paper are: math-ph
20
The categories of this paper are: acc-phys
22
The categories of this paper are: chao-dyn
17
The categories of this paper are: q-fin
0
The categories of this paper are: hep-ph
1
The categories of this paper are: cs
27
The categories of this paper are: comp-gas
37
The categories of this paper are: supr-con
3
The categories of this paper are: physics
18
The categories of this paper are: econ
28
The categories of this paper are: alg-geom
10
The categories of this paper are: q-bio
7
The categories of this paper are: hep-th
31
The categories of this paper are: ao-sci
26
The categories of this paper are: bayes-an
6
The categories of this paper are: astro-ph