DeepAI AI Chat
Log In Sign Up

Efficient Stepwise Selection in Decomposable Models

01/10/2013
by   Amol Deshpande, et al.
0

In this paper, we present an efficient way of performing stepwise selection in the class of decomposable models. The main contribution of the paper is a simple characterization of the edges that canbe added to a decomposable model while keeping the resulting model decomposable and an efficient algorithm for enumerating all such edges for a given model in essentially O(1) time per edge. We also discuss how backward selection can be performed efficiently using our data structures.We also analyze the complexity of the complete stepwise selection procedure, including the complexity of choosing which of the eligible dges to add to (or delete from) the current model, with the aim ofminimizing the Kullback-Leibler distance of the resulting model from the saturated model for the data.

READ FULL TEXT

page 1

page 2

page 3

page 4

08/13/2020

Inset Edges Effect and Average Distance of Trees

An added edge to a graph is called an inset edge. Predicting k inset edg...
05/10/2021

Near Neighbor Search via Efficient Average Distortion Embeddings

A recent series of papers by Andoni, Naor, Nikolov, Razenshteyn, and Wai...
10/21/2018

Distributed Approximate Distance Oracles

Data structures that allow efficient distance estimation (distance oracl...
08/13/2020

Some Preliminary Result About the Inset Edge and Average Distance of Trees

An added edge to a graph is called an inset edge. Predicting k inset edg...
09/04/2023

A Simple Pipeline for Orthogonal Graph Drawing

Orthogonal graph drawing has many applications, e.g., for laying out UML...
04/18/2023

Parallel Greedy Spanners

A t-spanner of a graph is a subgraph that t-approximates pairwise distan...
08/07/2023

Using Range-Revocable Pseudonyms to Provide Backward Unlinkability in the Edge (Extended Version)

In this paper we propose a novel abstraction that we have named Range-Re...