Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Efficient Cosmological Parameter Estimation with Hamiltonian Monte Carlo   Traditional Markov Chain Monte Carlo methods suffer from low acceptance rate,
slow mixing and low efficiency in high dimensions. Hamiltonian Monte Carlo
resolves this issue by avoiding the random walk. Hamiltonian Monte Carlo (HMC)
is a Markov chain Monte Carlo (MCMC) technique built upon the basic principle
of Hamiltonian mechanics. Hamiltonian dynamics allows the chain to move along
trajectories of constant energy, taking large jumps in the parameter space with
relatively inexpensive computations. This new technique improves the acceptance
rate by a factor of 4 and boosts up the efficiency by at least a factor of D in
a D-dimensional parameter space. Therefor shorter chains will be needed for a
reliable parameter estimation comparing to a traditional MCMC chain yielding
the same performance. Besides that, the HMC is well suited for sampling from
non-Gaussian and curved distributions which are very hard to sample from using
the traditional MCMC methods. The method is very simple to code and can be
easily plugged into standard parameter estimation codes such as CosmoMC. In
this paper we demonstrate how the HMC can be efficiently used in cosmological
parameter estimation.

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The categories of this paper are: quant-ph
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The categories of this paper are: q-alg
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The categories of this paper are: atom-ph
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The categories of this paper are: eess
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The categories of this paper are: cs
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The categories of this paper are: patt-sol
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The categories of this paper are: comp-gas
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The categories of this paper are: ao-sci
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The categories of this paper are: supr-con
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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: adap-org
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The categories of this paper are: nucl-ex
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The categories of this paper are: nucl-th
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The categories of this paper are: stat
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The categories of this paper are: q-fin