# Anthony C. ConstantinouQueen Mary, University of London | QMUL · School of Electronic Engineering and Computer Science

Anthony C. Constantinou

B.Sc, M.Sc, P.hD

## About

61

Publications

51,453

Reads

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1,140

Citations

Citations since 2017

## Publications

Publications (61)

Bayesian Networks (BNs) have become increasingly popular over the last few decades as a tool for reasoning under uncertainty in fields as diverse as medicine, biology, epidemiology, economics and the social sciences. This is especially true in real-world areas where we seek to answer complex questions based on hypothetical evidence to determine act...

In Bayesian Networks (BNs), the direction of edges is crucial for causal reasoning and inference. However, Markov equivalence class considerations mean it is not always possible to establish edge orientations, which is why many BN structure learning algorithms cannot orientate all edges from purely observational data. Moreover, latent confounders c...

Learning the structure of a Bayesian Network (BN) with score-based solutions involves exploring the search space of possible graphs and moving towards the graph that maximises a given objective function. Some algorithms offer exact solutions that guarantee to return the graph with the highest objective score, while others offer approximate solution...

Discovering and parameterising latent confounders represent important and challenging problems in causal structure learning and density estimation respectively. In this paper, we focus on both discovering and learning the distribution of latent confounders. This task requires solutions that come from different areas of statistics and machine learni...

Learning from data that contain missing values represents a common phenomenon in many domains. Relatively few Bayesian Network structure learning algorithms account for missing data, and those that do tend to rely on standard approaches that assume missing data are missing at random, such as the Expectation-Maximisation algorithm. Because missing d...

Causal Bayesian Networks provide an important tool for reasoning under uncertainty with potential application to many complex causal systems. Structure learning algorithms that can tell us something about the causal structure of these systems are becoming increasingly important. In the literature, the validity of these algorithms is often tested fo...

Discovering and parameterising latent confounders represent important and challenging problems in causal structure learning and density estimation respectively. In this paper, we focus on both discovering and learning the distribution of latent confounders. This task requires solutions that come from different areas of statistics and machine learni...

Score-based algorithms that learn the structure of Bayesian networks can be used for both exact and approximate solutions. While approximate learning scales better with the number of variables, it can be computationally expensive in the presence of high dimensional data. This paper describes an approximate algorithm that performs parallel sampling...

In Bayesian Networks (BNs), the direction of edges is crucial for causal reasoning and inference. However, Markov equivalence class considerations mean it is not always possible to establish edge orientations, which is why many BN structure learning algorithms cannot orientate all edges from purely observational data. Moreover, latent confounders c...

Despite the massive popularity of the Asian Handicap (AH) football (soccer) betting market, its efficiency has not been adequately studied by the relevant literature. This paper combines rating systems with Bayesian networks and presents the first published model specifically developed for prediction and assessment of the efficiency of the AH betti...

Learning the structure of a Bayesian Network (BN) with score-based solutions involves exploring the search space of possible graphs and moving towards the graph that maximises a given objective function. Some algorithms offer exact solutions that guarantee to return the graph with the highest objective score, while others offer approximate solution...

Bayesian Networks (BNs) have become increasingly popular over the last few decades as a tool for reasoning under uncertainty in fields as diverse as medicine, biology, epidemiology, economics and the social sciences. This is especially true in real-world areas where we seek to answer complex questions based on hypothetical evidence to determine act...

Learning from data that contain missing values represents a common phenomenon in many domains. Relatively few Bayesian Network structure learning algorithms account for missing data, and those that do tend to rely on standard approaches that assume missing data are missing at random, such as the Expectation-Maximisation algorithm. Because missing d...

Bayesian Networks (BNs) have become a powerful technology for reasoning under uncertainty, particularly in areas that require causal assumptions that enable us to simulate the effect of intervention. The graphical structure of these models can be determined by causal knowledge, learnt from data, or a combination of both. While it seems plausible th...

Numerous Bayesian Network (BN) structure learning algorithms have been proposed in the literature over the past few decades. Each publication makes an empirical or theoretical case for the algorithm proposed in that publication and results across studies are often inconsistent in their claims about which algorithm is ‘best’. This is partly because...

The graph of a BN can be machine learned, determined by causal knowledge, or a combination of both. In disciplines like bioinformatics, applying BN structure learning algorithms can reveal new insights that would otherwise remain unknown. However, these algorithms are less effective when the input data are limited in terms of sample size, which is...

Structure learning algorithms that learn the graph of a Bayesian network from observational data often do so by assuming the data correctly reflect the true distribution of the variables. However, this assumption does not hold in the presence of measurement error, which can lead to spurious edges. This is one of the reasons why the synthetic perfor...

Child mortality from preventable diseases such as pneumonia and diarrhoea in low and middle-income countries remains a serious global challenge. We combine knowledge with available Demographic and Health Survey (DHS) data from India, to construct Causal Bayesian Networks (CBNs) and investigate the factors associated with childhood diarrhoea. We mak...

Score-based algorithms that learn Bayesian Network (BN) structures provide solutions ranging from different levels of approximate learning to exact learning. Approximate solutions exist because exact learning is generally not applicable to networks of moderate or higher complexity. In general, approximate solutions tend to sacrifice accuracy for sp...

Latent variables may lead to spurious relationships that can be misinterpreted as causal relationships. In Bayesian Networks (BNs), this challenge is known as learning under causal insufficiency. Structure learning algorithms that assume causal insufficiency tend to reconstruct the ancestral graph of a BN, where bi-directed edges represent confound...

several algorithms have been proposed towards discovering the graphical structure of bayesian networks. most of these algorithms are restricted to observational data and some enable us to incorporate knowledge as constraints in terms of what can and cannot be discovered by an algorithm. a common type of such knowledge involves the temporal order of...

This paper builds on recent developments in Bayesian network (BN) structure learning under the controversial assumption that the input variables are dependent. This assumption can be viewed as a learning constraint geared towards cases where the input variables are known or assumed to be dependent. It addresses the problem of learning multiple disj...

Some structure learning algorithms have proven to be effective in reconstructing hypothetical Bayesian Network graphs from synthetic data. However, in their mission to maximise a scoring function, many become conservative and minimise edges discovered. While simplicity is desired, the output is often a graph that consists of multiple independent su...

Available at:
http://bayesian-ai.eecs.qmul.ac.uk/bayesys/
http://www.bayesys.com

Available at:
http://bayesian-ai.eecs.qmul.ac.uk/bayesys/
http://www.bayesys.com

Score-based algorithms that learn Bayesian Network (BN) structures provide solutions ranging from different levels of approximate learning to exact learning. Approximate solutions exist because exact learning is generally not applicable to networks of moderate or higher complexity. In general, approximate solutions tend to sacrifice accuracy for sp...

Latent variables may lead to spurious relationships that can be misinterpreted as causal relationships. In Bayesian Networks (BNs), this challenge is known as learning under causal insufficiency. Structure learning algorithms that assume causal insufficiency tend to reconstruct the ancestral graph of a BN, where bi-directed edges represent confound...

Numerous Bayesian Network (BN) structure learning algorithms have been proposed in the literature over the past few decades. Each publication makes an empirical or theoretical case for the algorithm proposed in that publication and results across studies are often inconsistent in their claims about which algorithm is 'best'. This is partly because...

This paper builds on recent developments in Bayesian network (BN) structure learning under the controversial assumption that the input variables are dependent. This assumption is geared towards real-world datasets that incorporate variables which are assumed to be dependent. It aims to address the problem of learning multiple disjoint subgraphs whi...

Despite the massive popularity of the Asian Handicap (AH) football betting market, it has not been adequately studied by the relevant literature. This paper combines rating systems with hybrid Bayesian networks and presents the first published model specifically developed for prediction and assessment of the AH betting market. The results are based...

This paper describes Simpson's paradox, and explains its serious implications for randomised control trials. In particular, we show that for any number of variables we can simulate the result of a controlled trial which uniformly points to one conclusion (such as 'drug is effective') for every possible combination of the variable states, but when a...

Child mortality from preventable diseases such as pneumonia and diarrhoea in low and middle-income countries remains a serious global challenge. We combine knowledge with available Demographic and Health Survey (DHS) data from India, to construct Bayesian Networks (BNs) and investigate the factors associated with childhood diarrhoea. We make use of...

Several structure learning algorithms have been proposed towards discovering causal or Bayesian Network (BN) graphs, which is a particularly challenging problem in AI. The performance of these algorithms is evaluated based on the relationship the learned graph has with respect to the ground truth graph. However, there is no agreed scoring function...

The paper describes Dolores, a model designed to predict football match outcomes in one country by observing football matches in multiple other countries. The model is a mixture of two methods: (a) dynamic ratings and (b) Hybrid Bayesian Networks. It was developed as part of the international special issue competition Machine Learning for Soccer. U...

Judea Pearl, a Turing Award prize winner, is a true giant of the field of computer science and artificial intelligence. To say that his new book with Dana Mackenzie is timely is, in our view, an understatement. Coming from somebody of his stature and being written for a general audience (unlike his previous books), means that the concerns we have h...

Scientific research is heavily driven by interest in discovering, assessing, and modelling cause-and-effect relationships as guides for action. Much of the research in discovering relationships between information is based on methods which focus on maximising the predictive accuracy of a target factor of interest from a set of other related factors...

Bayesian networks help us model and understand the many variables that inform our decision‐making processes. Anthony C. Constantinou and Norman Fenton explain how they work, how they are built and the pitfalls to avoid along the way Bayesian networks help us model and understand the many variables that inform our decision‐making processes. Anthony...

While decision trees are a popular formal and quantitative method for determining an optimal decision from a finite set of choices, for all but very simple problems they are computationally intractable. For this reason, Influence Diagrams (IDs) have been used as a more compact and efficient alternative. However, most algorithmic solutions assume th...

In decision theory models, Expected Value of Partial Perfect Information (EVPPI) is an important analysis technique that is used to identify the value of acquiring further information on individual variables. EVPPI can be used to prioritize the parts of a model that should be improved or identify the parts where acquiring additional data or expert...

In 2015 the British government announced a number of major tax reforms for individual landlords.
To give landlords time to adjust, some of these tax measures are being introduced
gradually from April 2017, with full effect in tax year 2020/21. The changes in taxation have
received much media attention since there has been widespread belief that the...

Despite recent promising developments with large datasets and machine learning, the idea that automation alone can discover all key relationships between factors of interest remains a challenging task. Indeed, in many real-world domains, experts can often understand and identify key relationships that data alone may fail to discover, no matter how...

Despite recent promising developments with large datasets and machine learning, the idea that automation alone can discover all key relationships between factors of interest remains a challenging task. Indeed, in many real-world domains, experts can often understand and identify key relationships that data alone may fail to discover, no matter how...

Background
Mental health professionals increasingly carry out risk assessments to prevent future violence by their patients. However, there are problems with accuracy and these assessments do not always translate into successful risk management.
Objectives
Our aim was to improve the accuracy of assessment and identify risk factors that are causal...

Objectives:
Inspired by real-world examples from the forensic medical sciences domain, we seek to determine whether a decision about an interventional action could be subject to amendments on the basis of some incomplete information within the model, and whether it would be worthwhile for the decision maker to seek further information prior to sug...

We show that existing Bayesian network (BN) modelling techniques cannot capture the correct intuitive reasoning in the important case when a set of mutually exclusive events need to be modelled as separate nodes instead of states of a single node. A previously proposed ‘solution’, which introduces a simple constraint node that enforces mutual exclu...

Successful implementation of major projects requires careful management of uncertainty and risk. Yet such uncertainty is rarely effectively calculated when analysing project costs and benefits. This paper presents a Bayesian network (BN) modelling framework to calculate the costs, benefits, and return on investment of a project over a specified tim...

When developing a causal probabilistic model, i.e. a Bayesian network (BN), it is common to incorporate expert knowledge of factors that are important for decision analysis but where historical data are unavailable or difficult to obtain. This paper focuses on the problem whereby the distribution of some continuous variable in a BN is known from da...

Objectives:
(1) To develop a rigorous and repeatable method for building effective Bayesian network (BN) models for medical decision support from complex, unstructured and incomplete patient questionnaires and interviews that inevitably contain examples of repetitive, redundant and contradictory responses; (2) To exploit expert knowledge in the BN...

The purpose of Medium Secure Services (MSS) is to provide accommodation, support and treatment to individuals with enduring mental health problems who usually come into contact with the criminal justice system. These individuals are, therefore, believed to pose a risk of violence to themselves as well as to other individuals. Assessing and managing...

Forensic medical practitioners and scientists have for several years sought improved decision support for determining and managing care and release of prisoners with mental health problems. Some of these prisoners can pose a serious threat of violence to society after release. It is, therefore, critical that the risk of violent reoffending is accur...

Objectives
To assess referee bias with respect to fouls and penalty kicks awarded by taking explanatory factors into consideration.
Design
We present a novel Bayesian network model for assessing referee bias with respect to fouls and penalty kicks awarded. The model is applied to the 2011-12 English Premier League season.
Method
Unlike previous s...

We present a Bayesian network (BN) model for forecasting Association Football match outcomes. Both objective and subjective information are considered for prediction, and we demonstrate how probabilities transform at each level of model component, whereby predictive distributions follow hierarchical levels of Bayesian inference. The model was used...

A gambling market is usually described as being inefficient if there are one or more betting strategies that generate profit, at a consistent rate, as a consequence of exploiting market flaws. This paper examines the online European football gambling market based on 14 European football leagues over a period of seven years, from season 2005/06 to 2...

A rating system provides relative measures of superiority between adversaries. We propose a novel and simple approach, which we call pi-rating, for dynamically rating Association Football teams solely on the basis of the relative discrepancies in scores through relevant match instances. The pi-rating system is applicable to any other sport where th...

A Bayesian network is a graphical probabilistic model that represents the conditional dependencies among uncertain variables, which can be both objective and subjective. We present a Bayesian network model for forecasting Association Football matches in which the subjective variables represent the factors that are important for prediction but which...

Despite the massive popularity of probabilistic (association) football forecasting models, and the relative simplicity of the outcome of such forecasts (they require only three probability values corresponding to home win, draw, and away win) there is no agreed scoring rule to determine their forecast accuracy. Moreover, the various scoring rules u...

Researchers have witnessed the great success in deterministic and perfect information domains. Intelligent pruning and evaluation techniques have been proven to be sufficient in providing outstanding intelligent decision making performance. However, processes that model uncertainty and risk for real-life situations have not met the same success. As...

Despite the increasing importance and popularity of association football forecasting systems there is no agreed method of evaluating their accuracy. We have classified the evaluators used into two broad categories: those which consider only the prediction for the observed outcome; and those which consider the predictions for the unobserved as well...

A gambling market is usually described as being inefficient if there are one or more betting strategies that generate profit, at a consistent rate, as a consequence of exploiting market flaws. This paper evaluates the efficiency of the Association Football betting market. In contrast to earlier studies, we primarily show that: a) the accuracy betwe...

## Projects

Projects (2)

To develop an open-source system that will enable end-users to quickly and efficiently generate Bayesian Decision Networks (BDNs) for fully optimised decision-making under uncertainty. Part of the system will be based on new hybrid constraint-based and search-and-score Bayesian Network (BN) structure learning algorithms. The system will allow users to incorporate their knowledge for information fusion with data, along with relevant decision support requirements for intervention to maximise utility and minimise risk. This will enable us to limit the levels of manual construction currently required when building BDNs. The system will be evaluated with decision problems from diverse areas including, but not limited to, sports, medicine, forensics, the UK housing market, and the UK financial market.

The project aims to improve evidence-based decision-making in areas such as medicine, law, forensics, and transport. What makes it radical is that it plans to do this in situations (common for critical risk assessment problems) where there is little or even no data, and hence where traditional statistics cannot be used. Our solution is to develop a method to systemize the way expert driven causal (Bayesian Network) models can be built and used effectively either in the absence of data or as a means of determining what future data is really required. Working with relevant domain experts, along with cognitive psychologists, our methods will be developed and tested experimentally on real-world critical decision-problems. The proposed research has the potential to both reduce at source much unnecessary data collection and improve the results of analysis of data that is collected. It has the potential to provide rigorous, rational, auditable, visible and quantified probabilistic arguments to support decision-making and recommendations in areas where currently only ‘gut-feel’ is possible. This could lead to: more rational and defensible strategic policy making by decision makers in government, financial, and other organisations; better medical diagnostics; better understanding of the impact of different types of legal and forensic evidence. The project will enable scientists, statisticians, medics and lawyers, to be better able to reason about probability and understand the role and limitations of data, making better decisions with less data.
http://bayes-knowledge.com/