this article presents a comparative study of marginalization in the cities of sabzevar, neishabour, torbat heidariye and gonaabaad. it also presents the history of marginalization and marginal settlement in the world, related theories and models. the article also talks about the international experiences about marginalization and marginal settlement. the writers of the article have first identi...

Inferential models (IMs) provide a general framework for prior-free, frequencycalibrated, posterior probabilistic inference. The fundamental idea is the use of unobservable auxiliary variables to describe the underlying uncertainty about the parameter of interest. When nuisance parameters are present, a marginalization step can reduce the dimension of the auxiliary variable, which in turn leads...

The marginal maximum a posteriori probability (MAP) estimation problem, which calculates the mode of the marginal posterior distribution of a subset of variables with the remaining variables marginalized, is an important inference problem in many models, such as those with hidden variables or uncertain parameters. Unfortunately, marginal MAP can be NP-hard even on trees, and has attracted less ...

Marginal MAP inference involves making MAP predictions in systems defined with latent variables or missing information. It is significantly more difficult than pure marginalization and MAP tasks, for which a large class of efficient and convergent variational algorithms, such as dual decomposition, exist. In this work, we generalize dual decomposition to a generic power sum inference task, whic...

This paper is a continuation of the authors’ theoretical investigation of inferential model (IMs); see Martin, Hwang and Liu (2010). The fundamental idea is that prior-free posterior probability-like inference with desirable long-run frequency properties can be achieved through a system based on predicting unobserved auxiliary variables. In Part I, an intermediate conditioning step was proposed...

The problem of probabilistic inference concerns answering queries about conditional and marginal probabilities in graphical models. Consider two disjoint subsets E and F of the nodes in a graphical model G. A query regarding marginal distribution p(xF ) can be calculated by the marginalization operation ∑ G\F p(x). A query regarding conditional distribution p(xF |xE) can be calculated by p(xF |...

This paper describes valuation-based systems for representing and solving discrete optimization problems. The information in an optimization problem is represented using variables, sample spaces of variables, a set of values, and func­ tions that map sample spaces of sets of variables to the set of values. The func­ tions, called valuations, represent the factors of an objective function. Solvi...

This paper describes an abstract framework called valuation network for computation of marginals using local computation. In valuation networks, we represent knowledge using functions called valuations. Making inferences involves using two operators called marginalization and combination. Marginalization tells us how to coarsen a valuation by eliminating some variables. Combination tells us how...

The multiple payments settlement systems available in the United States differ on several dimensions. The Fedwire Funds Service, a utility that operates a U.S. large-value paymentssettlement service, offers the fastest speed of settlement. Recognizing that payments differ in the urgency with which they need to be settled, Fedwire offers banks a decreasing block-price schedule. This approach all...

This paper is a continuation of the authors' theoretical investigation of inferential model (IMs); see Martin, Hwang and Liu (2010). The fundamental idea is that prior-free posterior probability-like inference with desirable long-run frequency properties can be achieved through a system based on predicting unobserved auxiliary variables. In Part I, an intermediate conditioning step was proposed...