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Sökning: WFRF:(Wijayatunga Priyantha 1967 )

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4.
  • Suhr, Ole Bernt, et al. (författare)
  • Amyloid fibril composition within hereditary Val30Met (p. Val50Met) transthyretin amyloidosis families
  • 2019
  • Ingår i: PLOS ONE. - : Public Library of Science (PLoS). - 1932-6203. ; 14:2
  • Tidskriftsartikel (refereegranskat)abstract
    • BACKGROUND: The amyloid fibril in hereditary transthyretin (TTR) Val30Met (pVal50Met) amyloid (ATTR Val30Met) amyloidosis is composed of either a mixture of full-length and TTR fragments (Type A) or of only full-length TTR (Type B). The type of amyloid fibril exerts an impact on the phenotype of the disease, and on the outcome of diagnostic procedures and therapy. The aim of the present study was to investigate if the type of amyloid fibril remains the same within ATTR Val30Met amyloidosis families.METHODS: Fifteen families were identified in whom at least two first-degree relatives had their amyloid fibril composition determined. The type of ATTR was determined by Western blot in all but two patients. For these two patients a positive 99mTc-3,3-diphosphono-1,2-propanodicarboxylic acid scintigraphy indicated ATTR Type A.RESULTS: In 14 of the 15 families, the same amyloid fibril composition was noted irrespective of differences in age at onset. In the one family, different ATTR fibril types was found in two brothers with similar ages at onset.CONCLUSIONS: Family predisposition appears to have an impact on amyloid fibril composition in members of the family irrespective of their age at onset of disease, but if genetically determined, the gene/genes are likely to be situated at another location than the TTR gene in the genome.
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  • Wijayatunga, Priyantha, 1967- (författare)
  • A geometric view on Pearson's correlation coefficient and a generalization of it to non-linear dependencies
  • 2016
  • Ingår i: Ratio Mathematica. - Italy : eiris. - 1592-7415 .- 2282-8214. ; 30, s. 3-21
  • Tidskriftsartikel (refereegranskat)abstract
    • Measuring strength or degree of statistical dependence between two random variables is a common problem in many domains. Pearson's correlation coefficient $\rho$ is an accurate measure of linear dependence. We show that $\rho$ is a normalized, Euclidean type distance between joint probability distribution of the two random variables and that when their independence is assumed while keeping their marginal distributions. And the normalizing constant is the geometric mean of two maximal  distances; each between the joint probability distribution when the full linear dependence is assumed while preserving respective marginal distribution and that when the independence is assumed. Usage of it  is  restricted to linear dependence because it is based on  Euclidean type distances that are generally not metrics and considered full dependence is linear. Therefore, we argue that if a suitable distance metric is used while considering all possible maximal dependences then it can measure any non-linear dependence.  But then, one must define all the full dependences.  Hellinger distance that is a metric can be used as the distance measure between probability distributions and obtain a generalization of $\rho$ for the discrete case.
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  • Wijayatunga, Priyantha, 1967-, et al. (författare)
  • Appraisal of companies with Bayesian networks
  • 2006
  • Ingår i: International Journal of Business Intelligence and Data Mining. - : InderScience Publishers. - 1743-8187. ; 1:3, s. 329-346
  • Tidskriftsartikel (refereegranskat)abstract
    • Appraisal of companies is an important business activity. We mainly apply Bayesian networks for this classification task for Japanese electric company data. Firstly, few standard statistical techniques are performed. Then Bayesian networks are applied in four steps: (1) for implementing a current procedure of economical experts, where economical variables are clustered and then summarised for computing a score for deciding the economical state of the company, (2) the same is done but with clustering of economical variables based on data, (3) the naive Bayes classifier and (4) an improved naive Bayes classifier through adjusting its conditional density of each feature variable given the class variable, which are initially obtained by maximum likelihood estimation. Adjustments are done by using the simulated annealing optimisation. Finally, a sensible way for appraisal of companies is discussed.
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  • Wijayatunga, Priyantha, 1967- (författare)
  • Book Review: Bayesian Theory and Applications
  • 2013
  • Ingår i: Qvintensen. - Stockholm : Svenska statistikfrämjandet. - 2000-1819. ; :4, s. 18-19
  • Recension (övrigt vetenskapligt/konstnärligt)
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  • Wijayatunga, Priyantha, 1967- (författare)
  • Causal Effect Estimation Methods
  • 2014
  • Ingår i: Journal of Statistical and Econometric Methods. - : Scienpress Ltd. - 2241-0376. ; 3:2, s. 153-170
  • Tidskriftsartikel (refereegranskat)abstract
    • Relationship between two popular modeling frameworks of causalinference from observational data, namely, causal graphical model andpotential outcome causal model is discussed. How some popular causaleffect estimators found in applications of the potential outcome causalmodel, such as inverse probability of treatment weighted estimator anddoubly robust estimator can be obtained by using the causal graphicalmodel is shown. We confine to the simple case of binary outcome andtreatment variables with discrete confounders and it is shown how togeneralize results to cases of continuous variables.
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9.
  • Wijayatunga, Priyantha, 1967- (författare)
  • Correlation and causation explained
  • 2013
  • Annan publikation (populärvet., debatt m.m.)abstract
    • Though the word correlation means usually how two quantities vary together, perhaps it may be due to extensive use of Pearson’s correlation coefficient. It is often associated with a linear relationship between the two. Even many scientific people misinterpret zero correlation as independence of the two quantities forgetting about what it really means; there is no linear association between the two quantities concerned.Furthermore many confuse correlation with causation, i.e., many interpret non–zero correlation as an implied causal relationship. There are many examples of it even in scientific literature. Here our point is not to discuss all these misinterpretations, but to look at another thing on correlation, that is also related to causation in some sense.
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  • Wijayatunga, Priyantha, 1967- (författare)
  • Data Deluge and Its Analysis Issues
  • 2016
  • Ingår i: Proceedings of the 2nd International Conference in Accounting Researchers and Educators (ICARE 2016). - Kelaniya : Department of Accountancy, University of Kelaniya. ; , s. 21-21
  • Konferensbidrag (refereegranskat)abstract
    • Current availability of enormous amount of data is mainly due to technological advances. They are useful drawing inferences for creating new businesses, formulation of new policies or revising existing ones, etc. However, much of analyses are performed either by subject domain experts implementing mathematical and computational models incorrectly or by mathematical and computational professionals, purely on data driven basis without paying required attention to the subject domain knowledge. Both of these exercises often result in incorrect inferences and therefore they may harm the society, especially when their inferences are used in practice. We argue that, in order to get valid inferences these two parties should work together. Here we briefly discuss some of the issues that the large-scale data analyses should take into account, especially in open data and big data. We also briefly discuss our solutions that are rather simple to implement.
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11.
  • Wijayatunga, Priyantha, 1967-, et al. (författare)
  • Discriminative Prediction of Adverse Events for Optimized Therapies Following Traumatic Brain Injury
  • 2019
  • Konferensbidrag (refereegranskat)abstract
    • Traumatic brain injury (TBI) causes temporary or perma- nent alteration in brain functions. At intensive care units, TBI patients are usually multimodally monitored, thus rendering large volumes of data on many physiological variables. For the physician, these data are difficult to interpret due to their complexity, speed and volume. Thus, computa- tional aids are recommended, e.g., for predicting patient’s clinical status in near future. In this article, we describe a probabilistic model that can be used for aiding physician’s decision making process in TBI patient care in real time. Our model tries to capture time varying patterns of patient’s clinical information. The model is built by using a discrimi- native model learning framework so that it can predict adverse clinical events with a higher level of accuracy. That is, our model is built so that prediction of certain desired events are given more attention than that of the other less important ones. This can be achieved by estimating model parameters in such a way, for e.g. using a cost function, when a suitable model structure has been selected, that again can be done dis- criminatively. However, such estimation procedures have no closed form solutions, so numerical optimization methods are used.
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12.
  • Wijayatunga, Priyantha, 1967-, et al. (författare)
  • Discussion on the meeting on 'Data visualization'
  • 2019
  • Ingår i: Journal of the Royal Statistical Society. - UK : Royal Statistical Society. - 0964-1998 .- 1467-985X. ; 182:2, s. 433-441
  • Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
    • Visualizing both conditional and marginal associations in contingency tables by using simple diagrams is discussed
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13.
  • Wijayatunga, Priyantha, 1967- (författare)
  • Discussion on the paper by Caron and Fox
  • 2017
  • Ingår i: Journal of The Royal Statistical Society Series B-statistical Methodology. - USA : John Wiley & Sons. - 1369-7412 .- 1467-9868. ; 79:5, s. 1359-1359
  • Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
    • A measure of dependence for graph models
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14.
  • Wijayatunga, Priyantha, 1967- (författare)
  • Discussion on the paper titled "Gaussian differential privacy"
  • 2022
  • Ingår i: Journal of The Royal Statistical Society Series B-statistical Methodology. - : John Wiley & Sons. - 1369-7412 .- 1467-9868. ; 84:1, s. 49-50
  • Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)
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15.
  • Wijayatunga, Priyantha, 1967- (författare)
  • On Associative Confounder Bias
  • 2015
  • Ingår i: Thirteenth Scandinavian Conference on Artificial Intelligence. - 9781614995883 - 9781614995890 ; , s. 157-166
  • Konferensbidrag (refereegranskat)abstract
    • Conditioning on some set of confounders that causally affect both treatmentand outcome variables can be sufficient for eliminating bias introduced by allsuch confounders when estimating causal effect of the treatment on the outcomefrom observational data. It is done by including them in propensity score modelin so-called potential outcome framework for causal inference whereas in causalgraphical modeling framework usual conditioning on them is done. However inthe former framework, it is confusing when modeler finds a variable that is noncausallyassociated with both the treatment and the outcome. Some argue that suchvariables should also be included in the analysis for removing bias. But others arguethat they introduce no bias so they should be excluded and conditioning onthem introduces spurious dependence between the treatment and the outcome, thusresulting extra bias in the estimation. We show that there may be errors in boththe arguments in different contexts. When such a variable is found neither of theactions may give the correct causal effect estimate. Selecting one action over theother is needed in order to be less wrong.We discuss how to select the better action.
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  • Wijayatunga, Priyantha, 1967- (författare)
  • Probabilistic Analysis of Balancing Scores for Causal Inference
  • 2015
  • Ingår i: Journal of Mathematics Research. - Canada : Canadian Center of Science and Education. - 1916-9795 .- 1916-9809. ; 7:2, s. 90-100
  • Tidskriftsartikel (refereegranskat)abstract
    • Propensity scores are often used for stratification of treatment and control groups of subjects in observational data to remove confounding bias when estimating of  causal effect of the treatment on an outcome in so-called potential outcome causal modeling framework. In this article, we try to get some insights into basic behavior of  the propensity scores in a probabilistic sense. We do a simple analysis of their usage confining to the case of discrete confounding covariates and outcomes. While making clear about behavior of the propensity score our analysis shows how the so-called prognostic score can be derived simultaneously. However the prognostic score is derived in a limited sense in the current literature whereas our derivation is more general and shows all possibilities of having the score. And we call it outcome score. We argue that application of both the propensity score and the outcome score is the most efficient way for  reduction of dimension in the confounding covariates as opposed to current belief that the propensity score alone is the most efficient way.
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20.
  • Wijayatunga, Priyantha, 1967- (författare)
  • Probabilistic Graphical Models and Their Inferences (Tutorial)
  • 2019
  • Ingår i: 2019 IEEE 4th International Workshops on Foundations and Applications of Self* Systems (FAS*W). - 9781728124063 ; , s. 251-252
  • Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
    • Probabilistic graphical models are useful for mod- elling stochastic phenomena for doing inferences and reasoning under uncertainty. Especially, chain graph models and Bayesian networks can be used as probabilistic expert systems where inferences can be done with junction tree algorithm, etc. And they can be extended to capture multi-stage decision contexts. Fundamentally these models capture (in)dependence structure of the context, but model learning is hard in practice. There are methods to do this, from simple independence test-based ones to more advanced score-based methods. When these models are used as classifiers, model learning can be done discriminatively, thus resulting higher classification accuracies in them.
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21.
  • Wijayatunga, Priyantha, 1967-, et al. (författare)
  • Probabilistic prediction of increased intracranial pressure in patients with severe traumatic brain injury
  • 2022
  • Ingår i: Scientific Reports. - : Nature Publishing Group. - 2045-2322. ; 12:1
  • Tidskriftsartikel (refereegranskat)abstract
    • Traumatic brain injury (TBI) causes alteration in brain functions. Generally, at intensive care units (ICU), intracranial pressure (ICP) is monitored and treated to avoid increases in ICP with associated poor clinical outcome. The aim was to develop a model which could predict future ICP levels of individual patients in the ICU, to warn treating clinicians before secondary injuries occur. A simple and explainable, probabilistic Markov model was developed for the prediction task ICP ≥ 20 mmHg. Predictions were made for 10-min intervals during 60 min, based on preceding hour of ICP. A prediction enhancement method was developed to compensate for data imbalance. The model was evaluated on 29 patients with severe TBI. With random data selection from all patients (80/20% training/testing) the specificity of the model was high (0.94–0.95) and the sensitivity good to high (0.73–0.87). Performance was similar (0.90–0.95 and 0.73–0.89 respectively) when the leave-oneout cross-validation was applied. The new model could predict increased levels of ICP in a reliable manner and the enhancement method further improved the predictions. Further advantages are the straightforward expandability of the model, enabling inclusion of other time series data and/or static parameters. Next step is evaluation on more patients and inclusion of parameters other than ICP.
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22.
  • Wijayatunga, Priyantha, 1967- (författare)
  • Probability, Paradoxes and Human Thinking
  • 2019
  • Ingår i: Proceedings of the 15th SweCog Conference. - Skövde, Sweden : University of Skövde. - 9789198366754 ; , s. 54-56
  • Konferensbidrag (refereegranskat)abstract
    • Probability, related calculations and its interpretations are sometimes hard for people to grasp. This may be due to unreasonable or counterintuitive situations that they find in them. Here I take few probability and statistical paradoxes and discuss how people sometimes find them unreasonable, counterintuitive, etc. Often the problems and confusions are solved when the probabilities are interpreted correctly.
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23.
  • Wijayatunga, Priyantha, 1967- (författare)
  • Resolution to four probability paradoxes: Two-envelope, Wallet-game, Sleeping Beauty and Newcomb’s
  • 2019
  • Ingår i: <em>Proceedings of The 34th International Workshop on Statistical Modelling 2019</em>, <em>Guimarães, Portugal, Volume II</em>. - Portugal. - 9789892096308 ; , s. 252-257
  • Konferensbidrag (refereegranskat)abstract
    • So-called two-envelope, wallet-game, Sleeping Beauty and Newcomb’s paradoxes are resolved through simple logical and analytical arguments. We stress the need of such simple solutions to them, due to current controversies around their solutions. Unnecessarily complicated or misleading solutions can be avoided if contexts of these problems are analyzed critically and perhaps with common- sense arguments. Our simple solutions are important for applied probability and statistical methods, especially for practitioners of them.
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24.
  • Wijayatunga, Priyantha, 1967- (författare)
  • Resolving the Lord's Paradox
  • 2017
  • Ingår i: Proceedings of the 32nd International Workshop on Statistical Modelling. - Groningen, Netherlands : International Workshop on Statistical Modelling (IWSM). ; , s. 223-226
  • Konferensbidrag (refereegranskat)abstract
    • An explanation to Lord’s paradox using ordinary least square regres- sion models is given. It is not a paradox at all, if the regression parameters are interpreted as predictive or as causal with stricter conditions and be aware of laws of averages. We use derivation of a super-model from a given sub-model, when its residuals can be modelled with other potential predictors as a solution. 
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25.
  • Wijayatunga, Priyantha, 1967- (författare)
  • Some cases of prediction and inference with uncertainty
  • 2022
  • Ingår i: Proceedings of the 14th international conference on soft computing and pattern recognition (SoCPaR 2022). - Cham : Springer Nature. - 9783031275241 - 9783031275234 ; , s. 265-274
  • Konferensbidrag (refereegranskat)abstract
    • Probability and statistical methods are a better tool for making scientific inferences and handling uncertainty in empirical contexts. We show how the uncertainties happen in inferences and predictions, and how to handle them easily in some cases. Starting with a couple of probability paradoxes, for giving the reader an idea about how tricky the application of the probability can be, a potential uncertainty in statistical significance testing is shown. How the predictions can be done effectively with the probabilistic approach while handing the uncertainties is presented. And finally, what the analyst needs to consider when doing discrete predictions is discussed through application of Simpson’s paradox. For the task effective use of causal assumptions is discussed.
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26.
  • Wijayatunga, Priyantha, 1967- (författare)
  • Viewing Simpson’s Paradox
  • 2014
  • Ingår i: Statistica & Applicazioni. - Italy. - 1824-6672. ; XII:2, s. 225-235
  • Tidskriftsartikel (refereegranskat)abstract
    • Well known Simpson’s paradox is puzzling and surprising for many, especially for the empirical researchersand users of statistics. However there is no surprise as far as mathematical details areconcerned. A lot more is written about the paradox but most of them are beyond the grasp of suchusers. This short article is about explaining the phenomenon in an easy way to grasp using simplealgebra and geometry. The mathematical conditions under which the paradox can occur are madeexplicit and a simple geometrical illustration is used to describe it. We consider the reversal of theassociation between two binary variables, say, X and Y by a third binary variable, say, Z. We showthat it is always possible to define Z algebraically for non-extreme dependence between X and Y,therefore occurrence of the paradox depends on identifying it with a practical meaning for it in agiven context of interest, that is up to the subject domain expert. And finally we discuss the paradoxin predictive contexts since in literature it is argued that the paradox is resolved using causal reasoning.
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