Search Results for author: Anthony C. Constantinou

Found 16 papers, 3 papers with code

Investigating the validity of structure learning algorithms in identifying risk factors for intervention in patients with diabetes

no code implementations21 Mar 2024 Sheresh Zahoor, Anthony C. Constantinou, Tim M Curtis, Mohammed Hasanuzzaman

Diabetes, a pervasive and enduring health challenge, imposes significant global implications on health, financial healthcare systems, and societal well-being.

Tuning structure learning algorithms with out-of-sample and resampling strategies

no code implementations24 Jun 2023 Kiattikun Chobtham, Anthony C. Constantinou

One of the challenges practitioners face when applying structure learning algorithms to their data involves determining a set of hyperparameters; otherwise, a set of hyperparameter defaults is assumed.

Discovery and density estimation of latent confounders in Bayesian networks with evidence lower bound

no code implementations11 Jun 2022 Kiattikun Chobtham, Anthony C. Constantinou

Discovering and parameterising latent confounders represent important and challenging problems in causal structure learning and density estimation respectively.

Computational Efficiency Density Estimation +1

Parallel Sampling for Efficient High-dimensional Bayesian Network Structure Learning

1 code implementation19 Feb 2022 Zhigao Guo, Anthony C. Constantinou

This paper describes an approximate algorithm that performs parallel sampling on Candidate Parent Sets (CPSs), and can be viewed as an extension of MINOBS which is a state-of-the-art algorithm for structure learning from high dimensional data.

Vocal Bursts Intensity Prediction

Hybrid Bayesian network discovery with latent variables by scoring multiple interventions

no code implementations20 Dec 2021 Kiattikun Chobtham, Anthony C. Constantinou, Neville K. Kitson

The algorithm assumes causal insufficiency in the presence of latent variables and produces a Partial Ancestral Graph (PAG).

Effective and efficient structure learning with pruning and model averaging strategies

no code implementations1 Dec 2021 Anthony C. Constantinou, Yang Liu, Neville K. Kitson, Kiattikun Chobtham, Zhigao Guo

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.

valid

A survey of Bayesian Network structure learning

no code implementations23 Sep 2021 Neville K. Kitson, Anthony C. Constantinou, Zhigao Guo, Yang Liu, Kiattikun Chobtham

This paper provides a comprehensive review of combinatoric algorithms proposed for learning BN structure from data, describing 74 algorithms including prototypical, well-established and state-of-the-art approaches.

Epidemiology

Greedy structure learning from data that contain systematic missing values

1 code implementation9 Jul 2021 Yang Liu, Anthony C. Constantinou

Learning from data that contain missing values represents a common phenomenon in many domains.

The impact of prior knowledge on causal structure learning

no code implementations31 Jan 2021 Anthony C. Constantinou, Zhigao Guo, Neville K. Kitson

Because the value of knowledge depends on what data are available, we illustrate the results both with limited and big data.

How do some Bayesian Network machine learned graphs compare to causal knowledge?

no code implementations25 Jan 2021 Anthony C. Constantinou, Norman Fenton, Martin Neil

Maximising score fitting is ineffective in the presence of limited sample size because the fitting becomes increasingly distorted with limited data, guiding algorithms towards graphical patterns that share higher fitting scores and yet deviate considerably from the true graph.

Model Selection

Improving Bayesian Network Structure Learning in the Presence of Measurement Error

1 code implementation19 Nov 2020 Yang Liu, Anthony C. Constantinou, Zhigao Guo

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.

Approximate learning of high dimensional Bayesian network structures via pruning of Candidate Parent Sets

no code implementations8 Jun 2020 Zhigao Guo, Anthony C. Constantinou

The results illustrate how different levels of pruning affect the learning speed relative to the loss in accuracy in terms of model fitting, and show that aggressive pruning may be required to produce approximate solutions for high complexity problems.

Bayesian network structure learning with causal effects in the presence of latent variables

no code implementations29 May 2020 Kiattikun Chobtham, Anthony C. Constantinou

Structure learning algorithms that assume causal insufficiency tend to reconstruct the ancestral graph of a BN, where bi-directed edges represent confounding and directed edges represent direct or ancestral relationships.

Learning Bayesian networks from demographic and health survey data

no code implementations2 Dec 2019 Neville Kenneth Kitson, Anthony C. Constantinou

Weaknesses in the survey methodology and data available, as well as the variability in the CBNs generated by the different algorithms, mean that it is not possible to learn a definitive CBN from data.

Evaluating structure learning algorithms with a balanced scoring function

no code implementations29 May 2019 Anthony C. Constantinou

Several structure learning algorithms have been proposed towards discovering causal or Bayesian Network (BN) graphs.

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