---
res:
bibo_abstract:
- 'Recent work has shown that probabilistic models based on pairwise interactions-in
the simplest case, the Ising model-provide surprisingly accurate descriptions
of experiments on real biological networks ranging from neurons to genes. Finding
these models requires us to solve an inverse problem: given experimentally measured
expectation values, what are the parameters of the underlying Hamiltonian? This
problem sits at the intersection of statistical physics and machine learning,
and we suggest that more efficient solutions are possible by merging ideas from
the two fields. We use a combination of recent coordinate descent algorithms with
an adaptation of the histogram Monte Carlo method, and implement these techniques
to take advantage of the sparseness found in data on real neurons. The resulting
algorithm learns the parameters of an Ising model describing a network of forty
neurons within a few minutes. This opens the possibility of analyzing much larger
data sets now emerging, and thus testing hypotheses about the collective behaviors
of these networks.@eng'
bibo_authorlist:
- foaf_Person:
foaf_givenName: Tamara
foaf_name: Broderick,Tamara
foaf_surname: Broderick
- foaf_Person:
foaf_givenName: Miroslav
foaf_name: Dudik,Miroslav
foaf_surname: Dudik
- foaf_Person:
foaf_givenName: Gasper
foaf_name: Gasper Tkacik
foaf_surname: Tkacik
foaf_workInfoHomepage: http://www.librecat.org/personId=3D494DCA-F248-11E8-B48F-1D18A9856A87
orcid: 0000-0002-6699-1455
- foaf_Person:
foaf_givenName: Robert
foaf_name: Schapire,Robert E
foaf_surname: Schapire
- foaf_Person:
foaf_givenName: William
foaf_name: Bialek, William S
foaf_surname: Bialek
bibo_volume: q-bio.QM
dct_date: 2007^xs_gYear
dct_publisher: ArXiv@
dct_title: Faster solutions of the inverse pairwise Ising problem@
...