7,675 research outputs found
Exploiting Causal Independence in Bayesian Network Inference
A new method is proposed for exploiting causal independencies in exact
Bayesian network inference. A Bayesian network can be viewed as representing a
factorization of a joint probability into the multiplication of a set of
conditional probabilities. We present a notion of causal independence that
enables one to further factorize the conditional probabilities into a
combination of even smaller factors and consequently obtain a finer-grain
factorization of the joint probability. The new formulation of causal
independence lets us specify the conditional probability of a variable given
its parents in terms of an associative and commutative operator, such as
``or'', ``sum'' or ``max'', on the contribution of each parent. We start with a
simple algorithm VE for Bayesian network inference that, given evidence and a
query variable, uses the factorization to find the posterior distribution of
the query. We show how this algorithm can be extended to exploit causal
independence. Empirical studies, based on the CPCS networks for medical
diagnosis, show that this method is more efficient than previous methods and
allows for inference in larger networks than previous algorithms.Comment: See http://www.jair.org/ for any accompanying file
Facilitating entry into shea processing: a study of two interventions in northern Ghana
There is considerable potential for the shea industry (Vitellaria paradoxa) to contribute to the economic empowerment of women in the Sahel Region of sub-Saharan Africa. This article examines interventions in Ghana's Upper West Region at two different processing stages of the value chain, intended to facilitate women's participation in, and enhance the benefits accruing from, shea harvesting and processing. We use the responses of the nut pickers and butter processors to qualitative and quantitative field research undertaken in 2010 to explore the constraints facing women's market participation. Results showed that mechanisms to link butter producers to markets and to sources of credit were key for the development of the shea value chain in a way that retains value locally and benefits rural producers. Complementary services also facilitated participation in the butter chains. For women to benefit, the ability to negotiate and influence the terms of trade between producers and buyers is important. Such market initiatives and interventions must be considered in the context of time management of diverse livelihood strategies. Also, how financial management and benefit sharing occur within households is sure to interact with the willingness of women to participate in new shea opportunities
Exploiting Contextual Independence In Probabilistic Inference
Bayesian belief networks have grown to prominence because they provide
compact representations for many problems for which probabilistic inference is
appropriate, and there are algorithms to exploit this compactness. The next
step is to allow compact representations of the conditional probabilities of a
variable given its parents. In this paper we present such a representation that
exploits contextual independence in terms of parent contexts; which variables
act as parents may depend on the value of other variables. The internal
representation is in terms of contextual factors (confactors) that is simply a
pair of a context and a table. The algorithm, contextual variable elimination,
is based on the standard variable elimination algorithm that eliminates the
non-query variables in turn, but when eliminating a variable, the tables that
need to be multiplied can depend on the context. This algorithm reduces to
standard variable elimination when there is no contextual independence
structure to exploit. We show how this can be much more efficient than variable
elimination when there is structure to exploit. We explain why this new method
can exploit more structure than previous methods for structured belief network
inference and an analogous algorithm that uses trees
Nonlinear c-axis transport in Bi_2Sr_2CaCu_2O_(8+d) from two-barrier tunneling
Motivated by the peculiar features observed through intrinsic tunneling
spectroscopy of BiSrCaCuO mesas in the normal state,
we have extended the normal state two-barrier model for the c-axis transport
[M. Giura et al., Phys. Rev. B {\bf 68}, 134505 (2003)] to the analysis of
curves. We have found that the purely normal-state model reproduces all
the following experimental features: (a) the parabolic -dependence of
in the high- region (above the conventional pseudogap temperature),
(b) the emergence and the nearly voltage-independent position of the "humps"
from this parabolic behavior lowering the temperature, and (c) the crossing of
the absolute curves at a characteristic voltage . Our
findings indicate that conventional tunneling can be at the origin of most of
the uncommon features of the c axis transport in
BiSrCaCuO. We have compared our calculations to
experimental data taken in severely underdoped and slightly underdoped
BiSrCaCuO small mesas. We have found good agreement
between the data and the calculations, without any shift of the calculated
dI/dV on the vertical scale. In particular, in the normal state (above
) simple tunneling reproduces the experimental dI/dV quantitatively.
Below quantitative discrepancies are limited to a simple rescaling of
the voltage in the theoretical curves by a factor 2. The need for such
modifications remains an open question, that might be connected to a change of
the charge of a fraction of the carriers across the pseudogap opening.Comment: 7 pages, 5 figure
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