Causal Inference from Statistical Data

The so-called kernel-based tests of independence are developed for automatic causal discovery between random variables from purely observational statistical data, i.e., without intervention. Beyond the independence relations, the complexity of conditional distriubtions is used as an additional inference principle of determining the causal ordering between variables. Experiments with simulated and real-world data show that the proposed methods surpass the state-of-the-art approaches

A Computational Model of Syntactic Processing: Ambiguity Resolution from Interpretation

Syntactic ambiguity abounds in natural language, yet humans have no difficulty coping with it. In fact, the process of ambiguity resolution is almost always unconscious. But it is not infallible, however, as example 1 demonstrates. 1. The horse raced past the barn fell. This sentence is perfectly grammatical, as is evident when it appears in the following context: 2. Two horses were being shown off to a prospective buyer. One was raced past a meadow. and the other was raced past a barn. ... Grammatical yet unprocessable sentences such as 1 are called `garden-path sentences.' Their existence provides an opportunity to investigate the human sentence processing mechanism by studying how and when it fails. The aim of this thesis is to construct a computational model of language understanding which can predict processing difficulty. The data to be modeled are known examples of garden path and non-garden path sentences, and other results from psycholinguistics. It is widely believed that there are two distinct loci of computation in sentence processing: syntactic parsing and semantic interpretation. One longstanding controversy is which of these two modules bears responsibility for the immediate resolution of ambiguity. My claim is that it is the latter, and that the syntactic processing module is a very simple device which blindly and faithfully constructs all possible analyses for the sentence up to the current point of processing. The interpretive module serves as a filter, occasionally discarding certain of these analyses which it deems less appropriate for the ongoing discourse than their competitors. This document is divided into three parts. The first is introductory, and reviews a selection of proposals from the sentence processing literature. The second part explores a body of data which has been adduced in support of a theory of structural preferences --- one that is inconsistent with the present claim. I show how the current proposal can be specified to account for the available data, and moreover to predict where structural preference theories will go wrong. The third part is a theoretical investigation of how well the proposed architecture can be realized using current conceptions of linguistic competence. In it, I present a parsing algorithm and a meaning-based ambiguity resolution method.Comment: 128 pages, LaTeX source compressed and uuencoded, figures separate macros: rotate.sty, lingmacros.sty, psfig.tex. Dissertation, Computer and Information Science Dept., October 199

Combining multiple types of intelligence to generate probability maps of moving targets

Drug addiction in the United States generates significant health, economic, and social costs. One of the prominent ways in which traffickers smuggle drugs into the United States is by maritime shipments from South America. In 1989 Joint Interagency Task Force South (JIATF-S) was established to fight these traffickers. JIATF-S collects information from multiple sources, which can be broadly classified into two categories. The first category is sensor-based sources that produce observations about possible targets (e.g., radar, sonar). These observations provide precise location and time but are susceptible to false positive and false negative errors regarding their content. The second category is human-based sources, including tips, messages and intercepted communications among humans. In addition to possible misinformation regarding the content of an event, such inputs are also susceptible to errors regarding the location and time of the event. In this thesis we develop a data fusion model that can assist JIATF-S in estimating the likelihood that a certain target (i.e., drug-smuggling vessel) is present at a certain location at a certain time and evaluate the reliability of the information source. The novelty of this thesis is manifested in a new probabilistic approach for utilizing human-generated intelligence, and in the way it is combined with sensor-generated intelligence.http://archive.org/details/combiningmultipl1094537751Captain, Israel Defense ForcesApproved for public release; distribution is unlimited