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Showing 1 to 7 of 7 for “"Independence Testing"”.
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Modern k-Nearest Neighbour Methods in Entropy Estimation, Independence Testing and Classification
… two random vectors, and its application to testing for independence. We propose tests for the two different situations of the marginal distributions being known or unknown and analyse their performance. Finally, we study the classical k-nearest neighbour classifier of Fix and Hodges (1951) …
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Microcomputer programming package for the assessment of multiattribute value functions
… of the decision making process. Algorithms for independence testing and parameter estimation have been developed for both continuous attributes and discrete attributes. Two separate packages, an additive value function package (DECISION) and a SMART technique package (SMART), are developed based …
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A new constraint-based algorithm to learn Bayesian network structure from data: Control of Spurious Pairwise Information (CSPI)
… requires an exponential number of conditional independence tests; several algorithms have been proposed in order to reduce the runtime of this procedure. We present a new constraint-based algorithm for learning Bayesian network structure from data, based on Control of Spurious Pairwise …
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Semiparametric Methods for Two Problems in Causal Inference using Machine Learning
… to doubly-robust methods and conditional independence testing. In the second chapter, we explore the doubly-robust estimation of the average partial effect — a generalisation of the linear coefficient in a (partially) linear model and a local measure of causal effect. This framework …
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Equitability and dependence
… for all non-trivial relationships using an independence test can yield too many results to be a useful approach. What is needed is a way of identifying a smaller set of "strongest" relationships, independent of relationship type (e.g., linear, exponential, etc.). The first goal of this work …
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On the finite sample complexity of causal discovery and the value of domain expertise
… discovery under the assumption of a conditional independence (CI) oracle: an oracle that can states whether two random variables are conditionally independent given another set of random variables. Practical implementations of this algorithm incorporate statistical tests for conditional …
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Unsupervised Learning : Model-guided and Model-agnostic Approaches
… is also estimated and applied to conditional independence testing. The above approaches to unsupervised learning do not assume any model for the data generation and learn it implicitly from data. However, in many real-world problems, one has domain knowledge about the data-generation process. …