Thomas D. Nielsen

Bio: Thomas D. Nielsen is an academic researcher from Aalborg University. The author has contributed to research in topics: Bayesian network & Influence diagram. The author has an hindex of 23, co-authored 116 publications receiving 1851 citations. Previous affiliations of Thomas D. Nielsen include University of Almería & Amateur Athletic Union.

Classification using Hierarchical Naïve Bayes models

Abstract: Classification problems have a long history in the machine learning literature. One of the simplest, and yet most consistently well-performing set of classifiers is the Naive Bayes models. However, an inherent problem with these classifiers is the assumption that all attributes used to describe an instance are conditionally independent given the class of that instance. When this assumption is violated (which is often the case in practice) it can reduce classification accuracy due to "information double-counting" and interaction omission. In this paper we focus on a relatively new set of models, termed Hierarchical Naive Bayes models. Hierarchical Naive Bayes models extend the modeling flexibility of Naive Bayes models by introducing latent variables to relax some of the independence statements in these models. We propose a simple algorithm for learning Hierarchical Naive Bayes models in the context of classification. Experimental results show that the learned models can significantly improve classification accuracy as compared to other frameworks.

A parallel algorithm for Bayesian network structure learning from large data sets

Abstract: This paper considers a parallel algorithm for Bayesian network structure learning from large data sets. The parallel algorithm is a variant of the well known PC algorithm. The PC algorithm is a constraint-based algorithm consisting of five steps where the first step is to perform a set of (conditional) independence tests while the remaining four steps relate to identifying the structure of the Bayesian network using the results of the (conditional) independence tests. In this paper, we describe a new approach to parallelization of the (conditional) independence testing as experiments illustrate that this is by far the most time consuming step. The proposed parallel PC algorithm is evaluated on data sets generated at random from five different real-world Bayesian networks. The algorithm is also compared empirically with a process-based approach where each process manages a subset of the data over all the variables on the Bayesian network. The results demonstrate that significant time performance improvements are possible using both approaches.

Symbolic and Quantitative Approaches to Reasoning with Uncertainty: 7th European Conference, ECSQARU 2003, Aalborg, Denmark, July 2-5, 2003. Proceedings

Abstract: Invited Papers.- Qualitative Decision Rules under Uncertainty.- Applications of Latent Class Analysis in Social Science Research.- Foundations of Uncertainty Concepts.- Transformations from Imprecise to Precise Probabilities.- A Representation Theorem and Applications.- On Modal Probability and Belief.- Bayesian Networks.- A Multi-layered Bayesian Network Model for Structured Document Retrieval.- Using Kappas as Indicators of Strength in Qualitative Probabilistic Networks.- Qualitative Bayesian Networks with Logical Constraints.- Introducing Situational Influences in QPNs.- Classification of Aerial Missions Using Hidden Markov Models.- Algorithms for Uncertainty Inference.- Dynamic Importance Sampling Computation in Bayesian Networks.- Morphing the Hugin and Shenoy-Shafer Architectures.- Learning.- Characterization of Inclusion Neighbourhood in Terms of the Essential Graph: Upper Neighbours.- Approximating Conditional MTE Distributions by Means of Mixed Trees.- Effective Dimensions of Partially Observed Polytrees.- Decision Graphs.- Applying Numerical Trees to Evaluate Asymmetric Decision Problems.- Mixed Influence Diagrams.- Decision Making Based on Sampled Disease Occurrence in Animal Herds.- Decision Network Semantics of Branching Constraint Satisfaction Problems.- Belief Functions.- Web of Trust: Applying Probabilistic Argumentation to Public-Key Cryptography.- A Comparison of Methods for Transforming Belief Function Models to Probability Models.- Fuzzy Matching and Evidential Reasoning.- Modeling Positive and Negative Pieces of Evidence in Uncertainty.- Directed Evidential Networks with Conditional Belief Functions.- Computational-Workload Based Binarization and Partition of Qualitative Markov Trees for Belief Combination.- Risk Assessment in Drinking Water Production Using Belief Functions.- Algebraic Structures Related to the Consensus Operator for Combining of Beliefs.- Fuzzy Sets.- Inclusion Measures in Intuitionistic Fuzzy Set Theory.- A Random Set Model for Fuzzy Labels.- On the Induction of Different Kinds of First-Order Fuzzy Rules.- Reasoning under Vagueness Expressed by Nuanced Statements.- Possibility Theory.- Partial Lattice-Valued Possibilistic Measures and Some Relations Induced by Them.- Coherent Conditional Probability as a Measure of Uncertainty of the Relevant Conditioning Events.- Decision Trees and Qualitative Possibilistic Inference: Application to the Intrusion Detection Problem.- Default Reasoning.- Multi-valued Conditional Events Avoid Lewis' Triviality Result.- Solving Semantic Problems with Odd-Length Cycles in Argumentation.- On the Relation between Reiter's Default Logic and Its (Major) Variants.- Belief Revision and Inconsistency Handling.- Probable Consistency Checking for Sets of Propositional Clauses.- On Iterated Revision in the AGM Framework.- Epistemic Logics for Information Fusion.- Logics.- Propositional Fusion Rules.- Preferential Logics for Reasoning with Graded Uncertainty.- Paraconsistent Reasoning via Quantified Boolean Formulas, II: Circumscribing Inconsistent Theories.- Modal (Logic) Paraconsistency.- A Formal Framework for Handling Conflicting Desires.- A Sequent Calculus for Skeptical Reasoning in Predicate Default Logic (Extended Abstract).- Probabilistic Lexicographic Entailment under Variable-Strength Inheritance with Overriding.- Demo Papers.- ABEL: An Interactive Tool for Probabilistic Argumentative Reasoning.- The Hugin Tool for Learning Bayesian Networks.

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Bayesian networks and decision graphs

Abstract: Probabilistic graphical models and decision graphs are powerful modeling tools for reasoning and decision making under uncertainty. As modeling languages they allow a natural specification of problem domains with inherent uncertainty, and from a computational perspective they support efficient algorithms for automatic construction and query answering. This includes belief updating, finding the most probable explanation for the observed evidence, detecting conflicts in the evidence entered into the network, determining optimal strategies, analyzing for relevance, and performing sensitivity analysis. The book introduces probabilistic graphical models and decision graphs, including Bayesian networks and influence diagrams. The reader is introduced to the two types of frameworks through examples and exercises, which also instruct the reader on how to build these models. The book is a new edition of Bayesian Networks and Decision Graphs by Finn V. Jensen. The new edition is structured into two parts. The first part focuses on probabilistic graphical models. Compared with the previous book, the new edition also includes a thorough description of recent extensions to the Bayesian network modeling language, advances in exact and approximate belief updating algorithms, and methods for learning both the structure and the parameters of a Bayesian network. The second part deals with decision graphs, and in addition to the frameworks described in the previous edition, it also introduces Markov decision processes and partially ordered decision problems. The authors also provide a well-founded practical introduction to Bayesian networks, object-oriented Bayesian networks, decision trees, influence diagrams (and variants hereof), and Markov decision processes. give practical advice on the construction of Bayesian networks, decision trees, and influence diagrams from domain knowledge. give several examples and exercises exploiting computer systems for dealing with Bayesian networks and decision graphs. present a thorough introduction to state-of-the-art solution and analysis algorithms. The book is intended as a textbook, but it can also be used for self-study and as a reference book.

Dynamic bayesian networks: representation, inference and learning

Abstract: Dynamic Bayesian Networks: Representation, Inference and Learning by Kevin Patrick Murphy Doctor of Philosophy in Computer Science University of California, Berkeley Professor Stuart Russell, Chair Modelling sequential data is important in many areas of science and engineering. Hidden Markov models (HMMs) and Kalman filter models (KFMs) are popular for this because they are simple and flexible. For example, HMMs have been used for speech recognition and bio-sequence analysis, and KFMs have been used for problems ranging from tracking planes and missiles to predicting the economy. However, HMMs and KFMs are limited in their “expressive power”. Dynamic Bayesian Networks (DBNs) generalize HMMs by allowing the state space to be represented in factored form, instead of as a single discrete random variable. DBNs generalize KFMs by allowing arbitrary probability distributions, not just (unimodal) linear-Gaussian. In this thesis, I will discuss how to represent many different kinds of models as DBNs, how to perform exact and approximate inference in DBNs, and how to learn DBN models from sequential data. In particular, the main novel technical contributions of this thesis are as follows: a way of representing Hierarchical HMMs as DBNs, which enables inference to be done in O(T ) time instead of O(T ), where T is the length of the sequence; an exact smoothing algorithm that takes O(log T ) space instead of O(T ); a simple way of using the junction tree algorithm for online inference in DBNs; new complexity bounds on exact online inference in DBNs; a new deterministic approximate inference algorithm called factored frontier; an analysis of the relationship between the BK algorithm and loopy belief propagation; a way of applying Rao-Blackwellised particle filtering to DBNs in general, and the SLAM (simultaneous localization and mapping) problem in particular; a way of extending the structural EM algorithm to DBNs; and a variety of different applications of DBNs. However, perhaps the main value of the thesis is its catholic presentation of the field of sequential data modelling.

Artificial neural networks

Abstract: Artificial neural networks (ANNs) constitute a class of flexible nonlinear models designed to mimic biological neural systems. In this entry, we introduce ANN using familiar econometric terminology and provide an overview of ANN modeling approach and its implementation methods. † Correspondence: Chung-Ming Kuan, Institute of Economics, Academia Sinica, 128 Academia Road, Sec. 2, Taipei 115, Taiwan; ckuan@econ.sinica.edu.tw. †† I would like to express my sincere gratitude to the editor, Professor Steven Durlauf, for his patience and constructive comments on early drafts of this entry. I also thank Shih-Hsun Hsu and Yu-Lieh Huang for very helpful suggestions. The remaining errors are all mine.