Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Information Geometric Approach to Bayesian Lower Error Bounds   Information geometry describes a framework where probability densities can be
viewed as differential geometry structures. This approach has shown that the
geometry in the space of probability distributions that are parameterized by
their covariance matrix is linked to the fundamentals concepts of estimation
theory. In particular, prior work proposes a Riemannian metric - the distance
between the parameterized probability distributions - that is equivalent to the
Fisher Information Matrix, and helpful in obtaining the deterministic
Cram\'{e}r-Rao lower bound (CRLB). Recent work in this framework has led to
establishing links with several practical applications. However, classical CRLB
is useful only for unbiased estimators and inaccurately predicts the mean
square error in low signal-to-noise (SNR) scenarios. In this paper, we propose
a general Riemannian metric that, at once, is used to obtain both Bayesian CRLB
and deterministic CRLB along with their vector parameter extensions. We also
extend our results to the Barankin bound, thereby enhancing their applicability
to low SNR situations.

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The categories of this paper are: astro-ph
0
The categories of this paper are: hep-ph
26
The categories of this paper are: bayes-an
21
The categories of this paper are: adap-org
37
The categories of this paper are: supr-con
12
The categories of this paper are: nucl-th
36
The categories of this paper are: cmp-lg
34
The categories of this paper are: chem-ph
16
The categories of this paper are: stat
27
The categories of this paper are: comp-gas
35
The categories of this paper are: mtrl-th
11
The categories of this paper are: hep-lat
28
The categories of this paper are: alg-geom
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The categories of this paper are: funct-an
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The categories of this paper are: solv-int
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The categories of this paper are: acc-phys