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Theory unification and graphical models in human categorization
"... Disparate, mutually incompatible theories of categorization are widespread in cognitive psychology. While there are various formal results connecting pairs of these theories, the primary research focus has been on particular empirical tests of people’s favorite theories. This chapter steps back from ..."
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Disparate, mutually incompatible theories of categorization are widespread in cognitive psychology. While there are various formal results connecting pairs of these theories, the primary research focus has been on particular empirical tests of people’s favorite theories. This chapter steps back from the question of which single theory (if any) is “right, ” and focuses
Keiding N. Graphical models for inference under outcomedependent sampling. Stat Sci 2010;25:368–87
"... Abstract. We consider situations where data have been collected such that the sampling depends on the outcome of interest and possibly further covariates, as for instance in casecontrol studies. Graphical models represent assumptions about the conditional independencies among the variables. By incl ..."
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Abstract. We consider situations where data have been collected such that the sampling depends on the outcome of interest and possibly further covariates, as for instance in casecontrol studies. Graphical models represent assumptions about the conditional independencies among the variables. By including a node for the sampling indicator, assumptions about sampling processes can be made explicit. We demonstrate how to read off such graphs whether consistent estimation of the association between exposure and outcome is possible. Moreover, we give sufficient graphical conditions for testing and estimating the causal effect of exposure on outcome. The practical use is illustrated with a number of examples.
2003]: ‘On World Poverty: Its Causes and Effects
 Food and Agricultural Organization (FAO) of the United Nations, Research Bulletin
, 2003
"... Recent advances in modeling directed acyclic graphs are used to sortout causal patterns among a set of thirteen measures deemed relevant to the incidence of world poverty. Crosssection measures of the percent of population living on one and two dollars or less per day from eighty low income countr ..."
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Recent advances in modeling directed acyclic graphs are used to sortout causal patterns among a set of thirteen measures deemed relevant to the incidence of world poverty. Crosssection measures of the percent of population living on one and two dollars or less per day from eighty low income countries are exposed to a battery of tests of conditional independence with respect to measures of economic and political freedom, income inequality, income per person, agricultural income, child mortality, birth rate, life expectancy, relative size of rural population, illiteracy rate, foreign aid as a percentage of national income, international trade as a percentage of national income and percentage of population that is undernourished. Motivation for the method of analysis precedes results. Results are presented as a graph that shows our measures of economic and political freedom, income inequality, illiteracy and agricultural income to be exogenous movers of poverty when measured as the percent of the population living on two dollars or less per day. Foreign aid and international trade are not connected to the other variables in the graph. Results on our measure of extreme poverty (people living on one dollar or less per day) show that such populations are immune from improvements in economic progress of the general economy. The “rising tide lifts all boats ” argument apparently doesn’t cover the extreme poor of our sample.
Identification and likelihood inference for recursive linear models with correlated errors
, 2007
"... In recursive linear models, the multivariate normal joint distribution of all variables exhibits a dependence structure induced by recursive systems of linear structural equations. Such models appear in particular in seemingly unrelated regressions, structural equation modelling, simultaneous equati ..."
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In recursive linear models, the multivariate normal joint distribution of all variables exhibits a dependence structure induced by recursive systems of linear structural equations. Such models appear in particular in seemingly unrelated regressions, structural equation modelling, simultaneous equation systems, and in Gaussian graphical modelling. We show that recursive linear models that are ‘bowfree’ are wellbehaved statistical models, namely, they are everywhere identifiable and form curved exponential families. Here, ‘bowfree ’ refers to models satisfying the condition that if a variable x occurs in the structural equation for y, then the errors for x and y are uncorrelated. For the computation of maximum likelihood estimates in ‘bowfree ’ recursive linear models we introduce the Residual Iterative Conditional Fitting (RICF) algorithm. Compared to existing algorithms RICF is easily implemented requiring only least squares computations, has clear convergence properties, and finds parameter estimates in closed form whenever possible. 1
Graphical Models as Languages for Computer Assisted Diagnosis And Decision Making
 Bibliography 375 Symbolic and Quantitative Approaches to Reasoning with Uncertainty : 6th European Conference, ECSQARU 2001
"... this paper we look at graphical models from this point of view. We introduce various kinds of graphical models, and the comprehensibility of their syntax and semantics is in focus ..."
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this paper we look at graphical models from this point of view. We introduce various kinds of graphical models, and the comprehensibility of their syntax and semantics is in focus
Structural Learning of Chain Graphs via Decomposition
"... Chain graphs present a broad class of graphical models for description of conditional independence structures, including both Markov networks and Bayesian networks as special cases. In this paper, we propose a computationally feasible method for the structural learning of chain graphs based on the i ..."
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Chain graphs present a broad class of graphical models for description of conditional independence structures, including both Markov networks and Bayesian networks as special cases. In this paper, we propose a computationally feasible method for the structural learning of chain graphs based on the idea of decomposing the learning problem into a set of smaller scale problems on its decomposed subgraphs. The decomposition requires conditional independencies but does not require the separators to be complete subgraphs. Algorithms for both skeleton recovery and complex arrow orientation are presented. Simulations under a variety of settings demonstrate the competitive performance of our method, especially when the underlying graph is sparse.
Graphical Models for Surrogates
"... Recently, it has been demonstrated that graphical models promise some potential for expressing ..."
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Recently, it has been demonstrated that graphical models promise some potential for expressing
Dora, a Robot Exploiting Probabilistic Knowledge under Uncertain Sensing for Efficient Object Search ∗
"... Dora, the robot, is trying to find object in its environment. Instead of just exhaustively searching everywhere, Dora is equipped with probabilistic reasoning, representations, and planning to exploit uncertain commonsense knowledge, such as that cornflakes are usually found in kitchens, while also ..."
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Dora, the robot, is trying to find object in its environment. Instead of just exhaustively searching everywhere, Dora is equipped with probabilistic reasoning, representations, and planning to exploit uncertain commonsense knowledge, such as that cornflakes are usually found in kitchens, while also accounting for the uncertainty of sensing in the realworld. Dora demonstrates how to combine task and observation planning in the presence of uncertainty by autonomously switching between contingent and sequential planning sessions. The demonstration emphasises the benefit of employing a robot with commonsense knowledge and the benefit of the switching planner.
Learning linear cyclic causal models with latent variables. Submitted. Available online from the authors’ homepages
, 2012
"... Identifying causeeffect relationships between variables of interest is a central problem in science. Given a set of experiments we describe a procedure that identifies linear models that may contain cycles and latent variables. We provide a detailed description of the model family, full proofs of t ..."
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Identifying causeeffect relationships between variables of interest is a central problem in science. Given a set of experiments we describe a procedure that identifies linear models that may contain cycles and latent variables. We provide a detailed description of the model family, full proofs of the necessary and sufficient conditions for identifiability, a search algorithm that is complete, and a discussion of what can be done when the identifiability conditions are not satisfied. The algorithm is comprehensively tested in simulations, comparing it to competing algorithms in the literature. Furthermore, we adapt the procedure to the problem of cellular network inference, applying it to the biologically realistic data of the DREAM challenges. The paper provides a full theoretical foundation for the causal discovery procedure first presented by Eberhardt et al. (2010) and Hyttinen et al. (2010).
Analysis of Dynamic Interrelationships between Transportation Rates and Grain Prices
"... verbatim copies of this document for noncommercial purposes by any means, provided that this copyright notice appears on all such copies. Analysis of Dynamic Interrelationships between Transportation Rates and Grain Prices Transportation rates are vital componenets in the structure of U.S. grain ex ..."
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verbatim copies of this document for noncommercial purposes by any means, provided that this copyright notice appears on all such copies. Analysis of Dynamic Interrelationships between Transportation Rates and Grain Prices Transportation rates are vital componenets in the structure of U.S. grain exports. In this paper we study the dynamic properties of corn and soybean prices, and barge, rail and ocean shipping rates using time series analysis on monthly 19922001 data. Using Error Correction Model and Directed Acyclic Graphs, we capture the interconnectivity between the transportation rates and grain prices at selected domestic and export markets. We find Illinois processor prices are important sources of price discovery for both corn and soybeans. Further, barge rates explain about 24 % of the variation in grain prices while rail rates explain about 1012 % of the variation in corn and soybean prices. Analysis of Dynamic Interrelationships between Transportation Rates and Grain