A tutorial on recursive models for analyzing and predicting path choice behavior

The problem at the heart of this tutorial consists in modeling the path choice behavior of network users. This problem has been extensively studied in transportation science, where it is known as the route choice problem. In this literature, individuals' choice of paths are typically predicted using discrete choice models. This article is a tutorial on a specific category of discrete choice models called recursive, and it makes three main contributions: First, for the purpose of assisting future research on route choice, we provide a comprehensive background on the problem, linking it to different fields including inverse optimization and inverse reinforcement learning. Second, we formally introduce the problem and the recursive modeling idea along with an overview of existing models, their properties and applications. Third, we extensively analyze illustrative examples from different angles so that a novice reader can gain intuition on the problem and the advantages provided by recursive models in comparison to path-based ones

Multiple sequence alignment based on set covers

We introduce a new heuristic for the multiple alignment of a set of sequences. The heuristic is based on a set cover of the residue alphabet of the sequences, and also on the determination of a significant set of blocks comprising subsequences of the sequences to be aligned. These blocks are obtained with the aid of a new data structure, called a suffix-set tree, which is constructed from the input sequences with the guidance of the residue-alphabet set cover and generalizes the well-known suffix tree of the sequence set. We provide performance results on selected BAliBASE amino-acid sequences and compare them with those yielded by some prominent approaches

Learning Bayesian Networks Using Fast Heuristics

This thesis addresses score-based learning of Bayesian networks from data using a few fast heuristics. The algorithmic implementation of the heuristics is able to learn size 30-40 networks in seconds and size 1000-2000 networks in hours. Two algorithms, which are devised by Scanagatta et al. and dubbed Independence Selection and Acyclic Selection OBS have the capacity of learning very large Bayesian networks without the liabilities of the traditional heuristics that require maximum in-degree or ordering constraints. The two algorithms are respectively called Insightful Searching and Acyclic Selection Obeying Boolean-matrix Sanctioning (acronym ASOBS) in this thesis. This thesis also serves as an expansion of the work of Scanagatta et al. by revealing a computationally simple ordering strategy called Randomised Pairing Greedy Weight (acronym RPGw) that works well as an adjunct along with ASOBS with corresponding experiment results, which show that ASOBS was able to score higher and faster with the help of RPGw. Insightful Searching, ASOBS, and RPGw together form a system that learns Bayesian networks from data very fast