Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Markov Chain Sampling for Non-linear State Space Models Using Embedded
  Hidden Markov Models   I describe a new Markov chain method for sampling from the distribution of
the state sequences in a non-linear state space model, given the observation
sequence. This method updates all states in the sequence simultaneously using
an embedded Hidden Markov model (HMM). An update begins with the creation of a
``pool'' of K states at each time, by applying some Markov chain update to the
current state. These pools define an embedded HMM whose states are indexes
within this pool. Using the forward-backward dynamic programming algorithm, we
can then efficiently choose a state sequence at random with the appropriate
probabilities from the exponentially large number of state sequences that pass
through states in these pools. I show empirically that when states at nearby
times are strongly dependent, embedded HMM sampling can perform better than
Metropolis methods that update one state at a time.

5
The categories of this paper are: gr-qc
13
The categories of this paper are: quant-ph
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The categories of this paper are: dg-ga
15
The categories of this paper are: nucl-ex
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The categories of this paper are: chao-dyn
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The categories of this paper are: adap-org
28
The categories of this paper are: alg-geom
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The categories of this paper are: solv-int
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The categories of this paper are: funct-an
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The categories of this paper are: ao-sci
14
The categories of this paper are: math-ph
10
The categories of this paper are: q-bio
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The categories of this paper are: cmp-lg
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The categories of this paper are: bayes-an
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The categories of this paper are: mtrl-th
20
The categories of this paper are: acc-phys