B. the same conditional probability distribution. We
BAYESIAN LEARNING MODULE:
Methods for learning Bayesian networks
can discover dependency structure between observed variables. Although these
methods are useful in many applications, they run into computational and
statistical problems in domains that involve a large number of variables. we
consider a solution that is applicable when many variables have similar
behavior. We introduce a new class of models, module networks, that explicitly
partition the variables into modules that share the same parents in the network
and the same conditional probability distribution. We define the semantics of
module networks, and describe an algorithm that learns the modules’ composition and their
dependency structure from data. Evaluation on real data in the domains of gene
expression and the stock market shows that module networks generalize better
than Bayesian networks, and that the learned module network structure reveals
regularities that are obscured in learned Bayesian networks.
Initial Causal Pathways
This subsection first introduces the
representation of causal pathways in the ST space, and then elaborates how to
generate initial causal pathways.
Gaussian Bayesian Network (GBN). GBN is a special form of Bayesian
network for probabilistic inference with continuous Gaussian variables in a
DAG, in which each variable is assumed as linear function of its parents 9.
The ST causal relations of air pollutants are encoded in a GBN-based graphical
model, to represent both local and ST dependencies. Here we choose GBN to model
the causalities because: 1) GBN provides a simple way to represent the
dependencies among multiple pollutants variables, both locally and in the ST
space. 2) GBN models continuous variables rather than discrete values. Due to
the sensors monitor the concentration of pollutants per hour; GBN could help
better capture the fine-grained knowledge through the dependencies of these
this subsection, in light of the extricated coordinated examples also,
applicant sensors from the example digging module for each poison Pcmsn, we utilize Pcmsn
to speak to consistent esteems in the graphical model. 3) The qualities of
urban information fit the GBN show well. As appeared in Fig. 7, the
dissemination of 1-hour distinction (current esteem short the esteem 1-hour
back) of air toxins and meteorological information obey Gaussian appropriation.
In the following areas, standardized 1-hour contrasts of time arrangement
information will be utilized as contributions for the model.