7,576 research outputs found
Structure learning of undirected graphical models for count data
Biological processes underlying the basic functions of a cell involve complex
interactions between genes. From a technical point of view, these interactions
can be represented through a graph where genes and their connections are,
respectively, nodes and edges. The main objective of this paper is to develop a
statistical framework for modelling the interactions between genes when the
activity of genes is measured on a discrete scale. In detail, we define a new
algorithm for learning the structure of undirected graphs, PC-LPGM, proving its
theoretical consistence in the limit of infinite observations. The proposed
algorithm shows promising results when applied to simulated data as well as to
real data
On the triplet anti-triplet symmetry in 3-3-1 models
We present a detailed discussion of the triplet anti-triplet symmetry in
3-3-1 models. The full set of conditions to realize this symmetry is provided,
which includes in particular the requirement that the two vacuum expectation
values of the two scalar triplets responsible for making the W and Z bosons
massive must be interchanged. We apply this new understanding to the
calculation of processes that have a Z-Z' mixing.Comment: 14 page
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