15,183 research outputs found
Smoothing and filtering with a class of outer measures
Filtering and smoothing with a generalised representation of uncertainty is
considered. Here, uncertainty is represented using a class of outer measures.
It is shown how this representation of uncertainty can be propagated using
outer-measure-type versions of Markov kernels and generalised Bayesian-like
update equations. This leads to a system of generalised smoothing and filtering
equations where integrals are replaced by supremums and probability density
functions are replaced by positive functions with supremum equal to one.
Interestingly, these equations retain most of the structure found in the
classical Bayesian filtering framework. It is additionally shown that the
Kalman filter recursion can be recovered from weaker assumptions on the
available information on the corresponding hidden Markov model
Towards a quantum theory of de Sitter space
We describe progress towards constructing a quantum theory of de Sitter space
in four dimensions. In particular we indicate how both particle states and
Schwarzschild de Sitter black holes can arise as excitations in a theory of a
finite number of fermionic oscillators. The results about particle states
depend on a conjecture about algebras of Grassmann variables, which we state,
but do not prove.Comment: JHEP3 LaTex - 19 page
Enrichment Procedures for Soft Clusters: A Statistical Test and its Applications
Clusters, typically mined by modeling locality of attribute spaces, are often evaluated for their ability to demonstrate āenrichmentā of categorical features. A cluster enrichment procedure evaluates the membership of a cluster for significant representation in pre-defined categories of interest. While classical enrichment procedures assume a hard clustering deļ¬nition, in this paper we introduce a new statistical test that computes enrichments for soft clusters. We demonstrate an application of this test in reļ¬ning and evaluating soft clusters for classification of remotely sensed images
Latent class analysis for segmenting preferences of investment bonds
Market segmentation is a key component of conjoint analysis which addresses consumer
preference heterogeneity. Members in a segment are assumed to be homogenous in their
views and preferences when worthing an item but distinctly heterogenous to members of other
segments. Latent class methodology is one of the several conjoint segmentation procedures
that overcome the limitations of aggregate analysis and a-priori segmentation. The main
benefit of Latent class models is that market segment membership and regression parameters
of each derived segment are estimated simultaneously. The Latent class model presented in
this paper uses mixtures of multivariate conditional normal distributions to analyze rating
data, where the likelihood is maximized using the EM algorithm. The application focuses on
customer preferences for investment bonds described by four attributes; currency, coupon
rate, redemption term and price. A number of demographic variables are used to generate
segments that are accessible and actionable.peer-reviewe
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