Journal articles: 'Probabilistic scoring'

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Machete, Reason L. "Contrasting probabilistic scoring rules." Journal of Statistical Planning and Inference 143, no. 10 (2013): 1781–90. http://dx.doi.org/10.1016/j.jspi.2013.05.012.

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Predd, Joel B., Robert Seiringer, Elliott H. Lieb, Daniel N. Osherson, H. Vincent Poor, and Sanjeev R. Kulkarni. "Probabilistic Coherence and Proper Scoring Rules." IEEE Transactions on Information Theory 55, no. 10 (2009): 4786–92. http://dx.doi.org/10.1109/tit.2009.2027573.

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Hughes, Gareth, and Fiona J. Burnett. "Evaluation of Probabilistic Disease Forecasts." Phytopathology® 107, no. 10 (2017): 1136–43. http://dx.doi.org/10.1094/phyto-01-17-0023-fi.

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The statistical evaluation of probabilistic disease forecasts often involves calculation of metrics defined conditionally on disease status, such as sensitivity and specificity. However, for the purpose of disease management decision making, metrics defined conditionally on the result of the forecast—predictive values—are also important, although less frequently reported. In this context, the application of scoring rules in the evaluation of probabilistic disease forecasts is discussed. An index of separation with application in the evaluation of probabilistic disease forecasts, described in the clinical literature, is also considered and its relation to scoring rules illustrated. Scoring rules provide a principled basis for the evaluation of probabilistic forecasts used in plant disease management. In particular, the decomposition of scoring rules into interpretable components is an advantageous feature of their application in the evaluation of disease forecasts.

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Mitchell, K., and C. A. T. Ferro. "Proper scoring rules for interval probabilistic forecasts." Quarterly Journal of the Royal Meteorological Society 143, no. 704 (2017): 1597–607. http://dx.doi.org/10.1002/qj.3029.

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Parry, Matthew. "Linear scoring rules for probabilistic binary classification." Electronic Journal of Statistics 10, no. 1 (2016): 1596–607. http://dx.doi.org/10.1214/16-ejs1150.

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Bröcker, Jochen, and Leonard A. Smith. "Scoring Probabilistic Forecasts: The Importance of Being Proper." Weather and Forecasting 22, no. 2 (2007): 382–88. http://dx.doi.org/10.1175/waf966.1.

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Abstract Questions remain regarding how the skill of operational probabilistic forecasts is most usefully evaluated or compared, even though probability forecasts have been a long-standing aim in meteorological forecasting. This paper explains the importance of employing proper scores when selecting between the various measures of forecast skill. It is demonstrated that only proper scores provide internally consistent evaluations of probability forecasts, justifying the focus on proper scores independent of any attempt to influence the behavior of a forecaster. Another property of scores (i.e., locality) is discussed. Several scores are examined in this light. There is, effectively, only one proper, local score for probability forecasts of a continuous variable. It is also noted that operational needs of weather forecasts suggest that the current concept of a score may be too narrow; a possible generalization is motivated and discussed in the context of propriety and locality.

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Vecer, Jan. "Dynamic Scoring: Probabilistic Model Selection Based on Utility Maximization." Entropy 21, no. 1 (2019): 36. http://dx.doi.org/10.3390/e21010036.

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We propose a novel approach of model selection for probability estimates that may be applied in time evolving setting. Specifically, we show that any discrepancy between different probability estimates opens a possibility to compare them by trading on a hypothetical betting market that trades probabilities. We describe the mechanism of such a market, where agents maximize some utility function which determines the optimal trading volume for given odds. This procedure produces supply and demand functions, that determine the size of the bet as a function of a trading probability. These functions are closed form for the choice of logarithmic and exponential utility functions. Having two probability estimates and the corresponding supply and demand functions, the trade matching these estimates happens at the intersection of the supply and demand functions. We show that an agent using correct probabilities will realize a profit in expectation when trading against any other set of probabilities. The expected profit realized by the correct view of the market probabilities can be used as a measure of information in terms of statistical divergence.

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Choi, Hyungwon, Brett Larsen, Zhen-Yuan Lin, et al. "SAINT: probabilistic scoring of affinity purification–mass spectrometry data." Nature Methods 8, no. 1 (2010): 70–73. http://dx.doi.org/10.1038/nmeth.1541.

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Diligenti, M., M. Gori, and M. Maggini. "A unified probabilistic framework for web page scoring systems." IEEE Transactions on Knowledge and Data Engineering 16, no. 1 (2004): 4–16. http://dx.doi.org/10.1109/tkde.2004.1264818.

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Murvai, J., K. Vlahovicek, and S. Pongor. "A simple probabilistic scoring method for protein domain identification." Bioinformatics 16, no. 12 (2000): 1155–56. http://dx.doi.org/10.1093/bioinformatics/16.12.1155.

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Wu, T. D., C. G. Nevill-Manning, and D. L. Brutlag. "Fast probabilistic analysis of sequence function using scoring matrices." Bioinformatics 16, no. 3 (2000): 233–44. http://dx.doi.org/10.1093/bioinformatics/16.3.233.

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Nahnsen, Sven, Andreas Bertsch, Jörg Rahnenführer, Alfred Nordheim, and Oliver Kohlbacher. "Probabilistic Consensus Scoring Improves Tandem Mass Spectrometry Peptide Identification." Journal of Proteome Research 10, no. 8 (2011): 3332–43. http://dx.doi.org/10.1021/pr2002879.

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Scheuerer, Michael, and Thomas M. Hamill. "Variogram-Based Proper Scoring Rules for Probabilistic Forecasts of Multivariate Quantities*." Monthly Weather Review 143, no. 4 (2015): 1321–34. http://dx.doi.org/10.1175/mwr-d-14-00269.1.

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Abstract Proper scoring rules provide a theoretically principled framework for the quantitative assessment of the predictive performance of probabilistic forecasts. While a wide selection of such scoring rules for univariate quantities exists, there are only few scoring rules for multivariate quantities, and many of them require that forecasts are given in the form of a probability density function. The energy score, a multivariate generalization of the continuous ranked probability score, is the only commonly used score that is applicable in the important case of ensemble forecasts, where the multivariate predictive distribution is represented by a finite sample. Unfortunately, its ability to detect incorrectly specified correlations between the components of the multivariate quantity is somewhat limited. In this paper the authors present an alternative class of proper scoring rules based on the geostatistical concept of variograms. The sensitivity of these variogram-based scoring rules to incorrectly predicted means, variances, and correlations is studied in a number of examples with simulated observations and forecasts; they are shown to be distinctly more discriminative with respect to the correlation structure. This conclusion is confirmed in a case study with postprocessed wind speed forecasts at five wind park locations in Colorado.

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Smith, Zachary J., and J. Eric Bickel. "Additive Scoring Rules for Discrete Sample Spaces." Decision Analysis 17, no. 2 (2020): 115–33. http://dx.doi.org/10.1287/deca.2019.0398.

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In this paper, we develop strictly proper scoring rules that may be used to evaluate the accuracy of a sequence of probabilistic forecasts. In practice, when forecasts are submitted for multiple uncertainties, competing forecasts are ranked by their cumulative or average score. Alternatively, one could score the implied joint distributions. We demonstrate that these measures of forecast accuracy disagree under some commonly used rules. Furthermore, and most importantly, we show that forecast rankings can depend on the selected scoring procedure. In other words, under some scoring rules, the relative ranking of probabilistic forecasts does not depend solely on the information content of those forecasts and the observed outcome. Instead, the relative ranking of forecasts is a function of the process by which those forecasts are evaluated. As an alternative, we describe additive and strongly additive strictly proper scoring rules, which have the property that the score for the joint distribution is equal to a sum of scores for the associated marginal and conditional distributions. We give methods for constructing additive rules and demonstrate that the logarithmic score is the only strongly additive rule. Finally, we connect the additive properties of scoring rules with analogous properties for a general class of entropy measures.

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Weir, Michael P., and Michael D. Rice. "TRII: A Probabilistic Scoring of Drosophila melanogaster Translation Initiation Sites." EURASIP Journal on Bioinformatics and Systems Biology 2010 (2010): 1–14. http://dx.doi.org/10.1155/2010/814127.

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Albarello, D., L. Peruzza, and V. D'Amico. "A scoring test on probabilistic seismic hazard estimates in Italy." Natural Hazards and Earth System Sciences 15, no. 1 (2015): 171–86. http://dx.doi.org/10.5194/nhess-15-171-2015.

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Abstract. Probabilistic estimates of seismic hazard represent a basic element for seismic risk reduction strategies and they are a key element of seismic regulation. Thus, it is important to select the most effective estimates among the available ones. An empirical scoring strategy is described here and is applied to a number of time-independent hazard estimates available in Italy both at national and regional scale. The scoring test is based on the comparison of outcomes provided by available computational models at a number of accelerometric sites where observations are available for 25 years. This comparison also allows identifying computational models that, providing outcomes that are in contrast with observations, should thus be discarded. The analysis shows that most of the hazard estimates proposed for Italy are not in contrast with observations and some computational models perform significantly better than others do. Furthermore, one can see that, at least locally, older estimates can perform better than the most recent ones. Finally, since the same computational model can perform differently depending on the region considered and on average return time of concern, no single model can be considered as the best-performing one. This implies that, moving along the hazard curve, the most suitable model should be selected by considering the specific problem of concern.

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Albarello, D., L. Peruzza, and V. D'Amico. "A scoring test on probabilistic seismic hazard estimates in Italy." Natural Hazards and Earth System Sciences Discussions 2, no. 9 (2014): 5721–57. http://dx.doi.org/10.5194/nhessd-2-5721-2014.

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Abstract. Probabilistic estimates of seismic hazard represent a basic element for planning seismic risk reduction strategies and they are key elements of seismic regulation. Due to its importance, it is mandatory to select most effective estimates among the available ones. A possible empirical scoring strategy is described here and is applied to a number of time-independent hazard estimates available in Italy both at national and regional scale. Scoring is based on the comparison of outcomes provided by available computational models at a number of accelerometric sites where observations are available for 25 years. This comparison also allows identifying computational models providing outcomes that contrast observations and thus should be discarded. The analysis shows that most of hazard estimates so far proposed for Italy do not contrast with observations and some computational models perform significantly better than the others do. Furthermore, one can see that, at least locally, older estimates can perform better that the most recent ones. Finally, since the same computational model can perform differently depending on the region considered and on average return time of concern, no single model can be considered as the best performing one. This implies that time-by-time, the most suitable model must be selected by considering the specific problem of concern.

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Maldonado, Sebastián, Georg Peters, and Richard Weber. "Credit scoring using three-way decisions with probabilistic rough sets." Information Sciences 507 (January 2020): 700–714. http://dx.doi.org/10.1016/j.ins.2018.08.001.

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Albarello, Dario, and Vera D’Amico. "Scoring and Testing Procedures Devoted to Probabilistic Seismic Hazard Assessment." Surveys in Geophysics 36, no. 2 (2015): 269–93. http://dx.doi.org/10.1007/s10712-015-9316-4.

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Capotorti, Andrea, and Eva Barbanera. "Credit scoring analysis using a fuzzy probabilistic rough set model." Computational Statistics & Data Analysis 56, no. 4 (2012): 981–94. http://dx.doi.org/10.1016/j.csda.2011.06.036.

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Samad, Manar D., and Sumen Sen. "A probabilistic approach to identifying run scoring advantage in the order of playing cricket." International Journal of Sports Science & Coaching 16, no. 4 (2021): 1011–20. http://dx.doi.org/10.1177/17479541211000333.

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In the game of cricket, the decision to bat first after winning the toss is often taken to make the best use of superior pitch conditions and set a big target for the opponent. However, the opponent may fail to show their natural batting performance in the second innings due to several factors, including deteriorated pitch conditions and excessive pressure of chasing a high target score. The advantage of batting first has been highlighted in the literature and expert opinions. However, the effect of batting and bowling order on match outcome has not been investigated well enough to recommend an adjustment of potential bias. This study proposes a probability-based model to study venue-specific scoring and chasing characteristics of teams with different match outcomes. A total of 1117 one-day international cricket matches held in ten popular venues are analyzed to show substantially high scoring likelihood when the winning team bat in the first innings. In a high scoring match, results suggest that the same bat-first winning team is very unlikely to score or chase the same high score if they bat in the second innings. We use the Bayesian rule to identify the bias in the scoring likelihood due to the playing order (bat-first versus bat-second). The bias is adjusted by revising the second innings target in a way that equalizes winning and run scoring likelihoods of both teams. The data and source code have been shared publicly for future research in creating competitive match outcomes by eliminating the advantage of batting order in run scoring.

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Benedetti, Riccardo. "Scoring Rules for Forecast Verification." Monthly Weather Review 138, no. 1 (2010): 203–11. http://dx.doi.org/10.1175/2009mwr2945.1.

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Abstract The problem of probabilistic forecast verification is approached from a theoretical point of view starting from three basic desiderata: additivity, exclusive dependence on physical observations (“locality”), and strictly proper behavior. By imposing such requirements and only using elementary mathematics, a univocal measure of forecast goodness is demonstrated to exist. This measure is the logarithmic score, based on the relative entropy between the observed occurrence frequencies and the predicted probabilities for the forecast events. Information theory is then used as a guide to choose the scoring-scale offset for obtaining meaningful and fair skill scores. Finally the Brier score is assessed and, for single-event forecasts, its equivalence to the second-order approximation of the logarithmic score is shown. The large part of the presented results are far from being new or original, nevertheless their use still meets with some resistance in the weather forecast community. This paper aims at providing a clear presentation of the main arguments for using the logarithmic score.

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Gonon, Gilles, Frédéric Bimbot, and Rémi Gribonval. "Probabilistic scoring using decision trees for fast and scalable speaker recognition." Speech Communication 51, no. 11 (2009): 1065–81. http://dx.doi.org/10.1016/j.specom.2009.02.007.

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Kerpedjiev, Peter, Jes Frellsen, Stinus Lindgreen, and Anders Krogh. "Adaptable probabilistic mapping of short reads using position specific scoring matrices." BMC Bioinformatics 15, no. 1 (2014): 100. http://dx.doi.org/10.1186/1471-2105-15-100.

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Reich, Nicholas G., Dave Osthus, Evan L. Ray, et al. "Reply to Bracher: Scoring probabilistic forecasts to maximize public health interpretability." Proceedings of the National Academy of Sciences 116, no. 42 (2019): 20811–12. http://dx.doi.org/10.1073/pnas.1912694116.

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Thorgeirsson, Adam Thor, and Frank Gauterin. "Probabilistic Predictions with Federated Learning." Entropy 23, no. 1 (2020): 41. http://dx.doi.org/10.3390/e23010041.

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Probabilistic predictions with machine learning are important in many applications. These are commonly done with Bayesian learning algorithms. However, Bayesian learning methods are computationally expensive in comparison with non-Bayesian methods. Furthermore, the data used to train these algorithms are often distributed over a large group of end devices. Federated learning can be applied in this setting in a communication-efficient and privacy-preserving manner but does not include predictive uncertainty. To represent predictive uncertainty in federated learning, our suggestion is to introduce uncertainty in the aggregation step of the algorithm by treating the set of local weights as a posterior distribution for the weights of the global model. We compare our approach to state-of-the-art Bayesian and non-Bayesian probabilistic learning algorithms. By applying proper scoring rules to evaluate the predictive distributions, we show that our approach can achieve similar performance as the benchmark would achieve in a non-distributed setting.

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Martin, Oliver, and Dietmar Schomburg. "Efficient comprehensive scoring of docked protein complexes using probabilistic support vector machines." Proteins: Structure, Function, and Bioinformatics 70, no. 4 (2007): 1367–78. http://dx.doi.org/10.1002/prot.21603.

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Jupp, Tim E., Rachel Lowe, Caio A. S. Coelho, and David B. Stephenson. "On the visualization, verification and recalibration of ternary probabilistic forecasts." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 370, no. 1962 (2012): 1100–1120. http://dx.doi.org/10.1098/rsta.2011.0350.

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We develop a graphical interpretation of ternary probabilistic forecasts in which forecasts and observations are regarded as points inside a triangle. Within the triangle, we define a continuous colour palette in which hue and colour saturation are defined with reference to the observed climatology. In contrast to current methods, forecast maps created with this colour scheme convey all of the information present in each ternary forecast. The geometrical interpretation is then extended to verification under quadratic scoring rules (of which the Brier score and the ranked probability score are well-known examples). Each scoring rule defines an associated triangle in which the square roots of the score , the reliability , the uncertainty and the resolution all have natural interpretations as root mean square distances. This leads to our proposal for a ternary reliability diagram in which data relating to verification and calibration can be summarized. We illustrate these ideas with data relating to seasonal forecasting of precipitation in South America, including an example of nonlinear forecast calibration. Codes implementing these ideas have been produced using the statistical software package R and are available from the authors.

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Dimitriadis, Timo, Tilmann Gneiting, and Alexander I. Jordan. "Stable reliability diagrams for probabilistic classifiers." Proceedings of the National Academy of Sciences 118, no. 8 (2021): e2016191118. http://dx.doi.org/10.1073/pnas.2016191118.

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A probability forecast or probabilistic classifier is reliable or calibrated if the predicted probabilities are matched by ex post observed frequencies, as examined visually in reliability diagrams. The classical binning and counting approach to plotting reliability diagrams has been hampered by a lack of stability under unavoidable, ad hoc implementation decisions. Here, we introduce the CORP approach, which generates provably statistically consistent, optimally binned, and reproducible reliability diagrams in an automated way. CORP is based on nonparametric isotonic regression and implemented via the pool-adjacent-violators (PAV) algorithm—essentially, the CORP reliability diagram shows the graph of the PAV-(re)calibrated forecast probabilities. The CORP approach allows for uncertainty quantification via either resampling techniques or asymptotic theory, furnishes a numerical measure of miscalibration, and provides a CORP-based Brier-score decomposition that generalizes to any proper scoring rule. We anticipate that judicious uses of the PAV algorithm yield improved tools for diagnostics and inference for a very wide range of statistical and machine learning methods.

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Carvalho, Arthur. "A Note on Sandroni-Shmaya Belief Elicitation Mechanism." Journal of Prediction Markets 10, no. 2 (2017): 14–21. http://dx.doi.org/10.5750/jpm.v10i2.1225.

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Incentive-compatible methods for eliciting beliefs, such as proper scoring rules, often rely on strong assumptions about how humans behave when making decisions under risk and uncertainty. For example, standard proper scoring rules assume that humans are risk neutral, an assumption that is often violated in practice. Under such an assumption, proper scoring rules induce honest reporting of beliefs, in a sense that experts maximize their expected scores from a proper scoring rule by honestly reporting their beliefs.Sandroni and Shmaya [Economic Theory Bulletin, volume 1, issue 1, 2013] suggested a remarkable mechanism based on proper scoring rules that induces honest reporting of beliefs without any assumptions on experts’ risk attitudes. In particular, the authors claimed that the mechanism relies only on the natural assumptions of probabilistic sophistication and dominance. We suggest in this paper that the reduction of compound lotteries axiom is another assumption required for Sandroni and Shmaya’s mechanism to induce honest reporting of beliefs. We further elaborate on the implications of such an extra assumption in light of recent findings regarding the reduction of compound lotteries axiom.

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MEMUSHAJ, ALKET, and TAREK M. SOBH. "USING GRAPHEME n-GRAMS IN SPELLING CORRECTION AND AUGMENTATIVE TYPING SYSTEMS." New Mathematics and Natural Computation 04, no. 01 (2008): 87–106. http://dx.doi.org/10.1142/s1793005708000970.

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Probabilistic language models have gained popularity in Natural Language Processing due to their ability to successfully capture language structures and constraints with computational efficiency. Probabilistic language models are flexible and easily adapted to language changes over time as well as to some new languages. Probabilistic language models can be trained and their accuracy strongly related to the availability of large text corpora. In this paper, we investigate the usability of grapheme probabilistic models, specifically grapheme n-grams models in spellchecking as well as augmentative typing systems. Grapheme n-gram models require substantially smaller training corpora and that is one of the main drivers for this thesis in which we build grapheme n-gram language models for the Albanian language. There are presently no available Albanian language corpora to be used for probabilistic language modeling. Our technique attempts to augment spellchecking and typing systems by utilizing grapheme n-gram language models in improving suggestion accuracy in spellchecking and augmentative typing systems. Our technique can be implemented in a standalone tool or incorporated in another tool to offer additional selection/scoring criteria.

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Lu, Jianping, Cun Wei, Jiang Wu, and Guiwu Wei. "TOPSIS Method for Probabilistic Linguistic MAGDM with Entropy Weight and Its Application to Supplier Selection of New Agricultural Machinery Products." Entropy 21, no. 10 (2019): 953. http://dx.doi.org/10.3390/e21100953.

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In multiple attribute group decision making (MAGDM) problems, uncertain decision information is well-represented by linguistic term sets (LTSs). These LTSs are easily converted into probabilistic linguistic sets (PLTSs). In this paper, a TOPSIS method is proposed for probabilistic linguistic MAGDM in which the attribute weights are completely unknown, and the decision information is in the form of probabilistic linguistic numbers (PLNs). First, the definition of the scoring function is used to solve the probabilistic linguistic entropy, which is then employed to objectively derive the attribute weights. Second, the optimal alternatives are determined by calculating the shortest distance from the probabilistic linguistic positive ideal solution (PLPIS) and on the other side the farthest distance of the probabilistic linguistic negative ideal solution (PLNIS). This proposed method extends the applications range of the traditional entropy-weighted method. Moreover, it doesn’t need the decision-maker to give the attribute weights in advance. Finally, a numerical example for supplier selection of new agricultural machinery products is used to illustrate the use of the proposed method. The result shows the approach is simple, effective and easy to calculate. The proposed method can contribute to the selection of suitable alternative successfully in other selection problems.

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Tashman, Zaid, Christoph Gorder, Sonali Parthasarathy, Mohamad M. Nasr-Azadani, and Rachel Webre. "Anomaly Detection System for Water Networks in Northern Ethiopia Using Bayesian Inference." Sustainability 12, no. 7 (2020): 2897. http://dx.doi.org/10.3390/su12072897.

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For billions of people living in remote and rural communities in the developing countries, small water systems are the only source of clean drinking water. Due to the rural nature of such water systems, site visits may occur infrequently. This means broken water systems can remain in a malfunctioning state for months, forcing communities to return to drinking unsafe water. In this work, we present a novel two-level anomaly detection system aimed to detect malfunctioning remote sensored water hand-pumps, allowing for a proactive approach to pump maintenance. To detect anomalies, we need a model of normal water usage behavior first. We train a multilevel probabilistic model of normal usage using approximate variational Bayesian inference to obtain a conditional probability distribution over the hourly water usage data. We then use this conditional distribution to construct a level-1 scoring function for each hourly water observation and a level-2 scoring function for each pump. Probabilistic models and Bayesian inference collectively were chosen for their ability to capture the high temporal variability in the water usage data at the individual pump level as well as their ability to estimate interpretable model parameters. Experimental results in this work have demonstrated that the pump scoring function is able to detect malfunctioning sensors as well as a change in water usage behavior allowing for a more responsive and proactive pump system maintenance.

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Hughes, Gareth, and Cairistiona Topp. "Probabilistic Forecasts: Scoring Rules and Their Decomposition and Diagrammatic Representation via Bregman Divergences." Entropy 17, no. 12 (2015): 5450–71. http://dx.doi.org/10.3390/e17085450.

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Mittelman, D., R. Sadreyev, and N. Grishin. "Probabilistic scoring measures for profile-profile comparison yield more accurate short seed alignments." Bioinformatics 19, no. 12 (2003): 1531–39. http://dx.doi.org/10.1093/bioinformatics/btg185.

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Bafna, V., and N. Edwards. "SCOPE: a probabilistic model for scoring tandem mass spectra against a peptide database." Bioinformatics 17, Suppl 1 (2001): S13—S21. http://dx.doi.org/10.1093/bioinformatics/17.suppl_1.s13.

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Albarello, D., and L. Peruzza. "Accounting for spatial correlation in the empirical scoring of probabilistic seismic hazard estimates." Bulletin of Earthquake Engineering 15, no. 6 (2016): 2571–85. http://dx.doi.org/10.1007/s10518-016-9961-0.

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James, Paul A., Rebecca Doherty, Marion Harris, et al. "Optimal Selection of Individuals for BRCA Mutation Testing: A Comparison of Available Methods." Journal of Clinical Oncology 24, no. 4 (2006): 707–15. http://dx.doi.org/10.1200/jco.2005.01.9737.

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Purpose Several methods have been described that estimate the likelihood that a family history of cancer is a result of a mutation in the BRCA1 or BRCA2 genes. We examined the performance of six different methods with the aim of identifying an optimal strategy for selecting individuals for mutation testing in clinical practice. Patients and Methods Two hundred fifty-seven families who had completed BRCA1 and BRCA2 mutation screening were assessed by six models representing the major methodologies used to assess the likelihood of a pathogenic mutation. The performance of each method as a selection criterion was compared with the result of mutation testing to produce sensitivity, specificity, and receiver operating curve data. The impact of incorporating breast cancer pathology data in the assessment was also analyzed. Results The highest accuracy was achieved by the Bayesian probabilistic model (BRCAPRO). The formal probabilistic methods were significantly more accurate than clinical scoring methods. The methods were further improved by the incorporation of information on breast cancer pathology (tumor grade and estrogen receptor/progesterone receptor status). The resulting combined probability figure was highly accurate when selecting individuals for BRCA1 testing. Some BRCA2 mutation carriers were missed by all of the models examined. Conclusion Formal probabilistic models provide significantly greater accuracy in the selection of families for gene testing than the use of clinical criteria or scoring methods. The accuracy is further enhanced by incorporating information on the pathology of breast cancers occurring in the families.

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Kenig, Batya, and Benny Kimelfeld. "Approximate Inference of Outcomes in Probabilistic Elections." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 2061–68. http://dx.doi.org/10.1609/aaai.v33i01.33012061.

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We study the complexity of estimating the probability of an outcome in an election over probabilistic votes. The focus is on voting rules expressed as positional scoring rules, and two models of probabilistic voters: the uniform distribution over the completions of a partial voting profile (consisting of a partial ordering of the candidates by each voter), and the Repeated Insertion Model (RIM) over the candidates, including the special case of the Mallows distribution. Past research has established that, while exact inference of the probability of winning is computationally hard (#P-hard), an additive polynomial-time approximation (additive FPRAS) is attained by sampling and averaging. There is often, though, a need for multiplicative approximation guarantees that are crucial for important measures such as conditional probabilities. Unfortunately, a multiplicative approximation of the probability of winning cannot be efficient (under conventional complexity assumptions) since it is already NP-complete to determine whether this probability is nonzero. Contrastingly, we devise multiplicative polynomial-time approximations (multiplicative FPRAS) for the probability of the complement event, namely, losing the election.

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Žitnik, Marinka, and Blaž Zupan. "Gene network inference by probabilistic scoring of relationships from a factorized model of interactions." Bioinformatics 30, no. 12 (2014): i246—i254. http://dx.doi.org/10.1093/bioinformatics/btu287.

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Dag, Ali, Kazim Topuz, Asil Oztekin, Serkan Bulur, and Fadel M. Megahed. "A probabilistic data-driven framework for scoring the preoperative recipient-donor heart transplant survival." Decision Support Systems 86 (June 2016): 1–12. http://dx.doi.org/10.1016/j.dss.2016.02.007.

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Allam, Ahmed, Peter J. Schulz, and Michael Krauthammer. "Toward automated assessment of health Web page quality using the DISCERN instrument." Journal of the American Medical Informatics Association 24, no. 3 (2016): 481–87. http://dx.doi.org/10.1093/jamia/ocw140.

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Background: As the Internet becomes the number one destination for obtaining health-related information, there is an increasing need to identify health Web pages that convey an accurate and current view of medical knowledge. In response, the research community has created multicriteria instruments for reliably assessing online medical information quality. One such instrument is DISCERN, which measures health Web page quality by assessing an array of features. In order to scale up use of the instrument, there is interest in automating the quality evaluation process by building machine learning (ML)-based DISCERN Web page classifiers. Objective: The paper addresses 2 key issues that are essential before constructing automated DISCERN classifiers: (1) generation of a robust DISCERN training corpus useful for training classification algorithms, and (2) assessment of the usefulness of the current DISCERN scoring schema as a metric for evaluating the performance of these algorithms. Methods: Using DISCERN, 272 Web pages discussing treatment options in breast cancer, arthritis, and depression were evaluated and rated by trained coders. First, different consensus models were compared to obtain a robust aggregated rating among the coders, suitable for a DISCERN ML training corpus. Second, a new DISCERN scoring criterion was proposed (features-based score) as an ML performance metric that is more reflective of the score distribution across different DISCERN quality criteria. Results: First, we found that a probabilistic consensus model applied to the DISCERN instrument was robust against noise (random ratings) and superior to other approaches for building a training corpus. Second, we found that the established DISCERN scoring schema (overall score) is ill-suited to measure ML performance for automated classifiers. Conclusion: Use of a probabilistic consensus model is advantageous for building a training corpus for the DISCERN instrument, and use of a features-based score is an appropriate ML metric for automated DISCERN classifiers. Availability: The code for the probabilistic consensus model is available at https://bitbucket.org/A_2/em_dawid/.

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Cui, Yimin, Junmei Li, Wei Zhao, and Cheng Luan. "Research on Network Security Quantitative Model Based on Probabilistic Attack Graph." ITM Web of Conferences 24 (2019): 02003. http://dx.doi.org/10.1051/itmconf/20192402003.

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In order to identify the threat of computer network security and evaluate its fragility comprehensively, the related factors of network security are studied, and the methods based on attack graph are improved. Based on the attribute attack graph, the probabilistic attack graph model is generated by adding various factors which affect network security. The model uses security equipment performance data, common vulnerability scoring system data and etc. to calculate priori probability, finally obtains the network security index, and carries on the exploratory analysis. The experimental results show that the model is feasible and effective. Compared with other vulnerability assessment methods, the model has the characteristics of comprehensive evaluation and concise calculation.

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Borovsky, D., G. Gotsch, G. Joseph, and Chr Zywietz. "Methodology of ECG Interpretation in the Hannover Program." Methods of Information in Medicine 29, no. 04 (1990): 375–85. http://dx.doi.org/10.1055/s-0038-1634800.

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AbstractThe Hannover ECG program HES has been designed for measurement and interpretation of resting and (moderate) exercise electrocardiograms. In the signal analysis part the program follows an averaging strategy. For diagnostic classification a hybrid model with decision trees and scoring algorithms, and with multivariate probabilistic tests for derivation of category A statements is applied. The multivariate classification technique allows to adjust sensitivity and specificity for specific application areas without changing the diagnostic criteria.

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Gunaydin, Hakan. "Probabilistic Approach to Generating MPOs and Its Application as a Scoring Function for CNS Drugs." ACS Medicinal Chemistry Letters 7, no. 1 (2015): 89–93. http://dx.doi.org/10.1021/acsmedchemlett.5b00390.

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Liu, Jianxin, Yushui Geng, Jing Zhao, Kang Zhang, and Wenxiao Li. "Image Semantic Segmentation Use Multiple-Threshold Probabilistic R-CNN with Feature Fusion." Symmetry 13, no. 2 (2021): 207. http://dx.doi.org/10.3390/sym13020207.

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With continuous developments in deep learning, image semantic segmentation technology has also undergone great advancements and been widely used in many fields with higher segmentation accuracy. This paper proposes an image semantic segmentation algorithm based on a deep neural network. Based on the Mask Scoring R-CNN, this algorithm uses a symmetrical feature pyramid network and adds a multiple-threshold architecture to improve the sample screening precision. We employ a probability model to optimize the mask branch of the model further to improve the algorithm accuracy for the segmentation of image edges. In addition, we adjust the loss function so that the experimental effect can be optimized. The experiments reveal that the algorithm improves the results.

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León-Justel, Antonio, Ainara Madrazo-Atutxa, Ana I. Alvarez-Rios, et al. "A Probabilistic Model for Cushing’s Syndrome Screening in At-Risk Populations: A Prospective Multicenter Study." Journal of Clinical Endocrinology & Metabolism 101, no. 10 (2016): 3747–54. http://dx.doi.org/10.1210/jc.2016-1673.

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Context: Cushing’s syndrome (CS) is challenging to diagnose. Increased prevalence of CS in specific patient populations has been reported, but routine screening for CS remains questionable. To decrease the diagnostic delay and improve disease outcomes, simple new screening methods for CS in at-risk populations are needed. Objective: To develop and validate a simple scoring system to predict CS based on clinical signs and an easy-to-use biochemical test. Design: Observational, prospective, multicenter. Setting: Referral hospital. Patients: A cohort of 353 patients attending endocrinology units for outpatient visits. Interventions: All patients were evaluated with late-night salivary cortisol (LNSC) and a low-dose dexamethasone suppression test for CS. Main Outcome Measures: Diagnosis or exclusion of CS. Results: Twenty-six cases of CS were diagnosed in the cohort. A risk scoring system was developed by logistic regression analysis, and cutoff values were derived from a receiver operating characteristic curve. This risk score included clinical signs and symptoms (muscular atrophy, osteoporosis, and dorsocervical fat pad) and LNSC levels. The estimated area under the receiver operating characteristic curve was 0.93, with a sensitivity of 96.2% and specificity of 82.9%. Conclusions: We developed a risk score to predict CS in an at-risk population. This score may help to identify at-risk patients in non-endocrinological settings such as primary care, but external validation is warranted.

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Sathiya, B., and T. V. Geetha. "Automatic Ontology Learning from Multiple Knowledge Sources of Text." International Journal of Intelligent Information Technologies 14, no. 2 (2018): 1–21. http://dx.doi.org/10.4018/ijiit.2018040101.

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The prime textual sources used for ontology learning are a domain corpus and dynamic large text from web pages. The first source is limited and possibly outdated, while the second is uncertain. To overcome these shortcomings, a novel ontology learning methodology is proposed to utilize the different sources of text such as a corpus, web pages and the massive probabilistic knowledge base, Probase, for an effective automated construction of ontology. Specifically, to discover taxonomical relations among the concept of the ontology, a new web page based two-level semantic query formation methodology using the lexical syntactic patterns (LSP) and a novel scoring measure: Fitness built on Probase are proposed. Also, a syntactic and statistical measure called COS (Co-occurrence Strength) scoring, and Domain and Range-NTRD (Non-Taxonomical Relation Discovery) algorithms are proposed to accurately identify non-taxonomical relations(NTR) among concepts, using evidence from the corpus and web pages.

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MARAGOUDAKIS, MANOLIS, ARISTOMENIS THANOPOULOS, KYRIAKOS SGARBAS, and NIKOS FAKOTAKIS. "DOMAIN KNOWLEDGE ACQUISITION AND PLAN RECOGNITION BY PROBABILISTIC REASONING." International Journal on Artificial Intelligence Tools 13, no. 02 (2004): 333–65. http://dx.doi.org/10.1142/s0218213004001570.

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This paper introduces a statistical framework for extracting medical domain knowledge from heterogeneous corpora. The acquired information is incorporated into a natural language understanding agent and applied to DIKTIS, an existing web-based educational dialogue system for the chemotherapy of nosocomial and community acquired pneumonia, aiming at providing a more intelligent natural language interaction. Unlike the majority of existing dialogue understanding engines, the presented system automatically encodes semantic representation of a user's query using Bayesian networks. The structure of the networks is determined from annotated dialogue corpora using the Bayesian scoring method, thus eliminating the tedious and costly process of manually coding domain knowledge. The conditional probability distributions are estimated during a training phase using data obtained from the same set of dialogue acts. In order to cope with words absent from our restricted dialogue corpus, a separate offline module was incorporated, which estimates their semantic role from both medical and general raw text corpora, correlating them with known lexical-semantically similar words or predefined topics. Lexical similarity is identified on the basis of both contextual similarity and co-occurrence in conjunctive expressions. The evaluation of the platform was performed against the existing language natural understanding module of DIKTIS, the architecture of which is based on manually embedded domain knowledge.

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Pizzo, Anaïs, Pascal Teyssere, and Long Vu-Hoang. "Boosted Gaussian Bayes Classifier and its application in bank credit scoring." Journal of Advanced Engineering and Computation 2, no. 2 (2018): 131. http://dx.doi.org/10.25073/jaec.201822.193.

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With the explosion of computer science in the last decade, data banks and networksmanagement present a huge part of tomorrows problems. One of them is the development of the best classication method possible in order to exploit the data bases. In classication problems, a representative successful method of the probabilistic model is a Naïve Bayes classier. However, the Naïve Bayes effectiveness still needs to be upgraded. Indeed, Naïve Bayes ignores misclassied instances instead of using it to become an adaptive algorithm. Different works have presented solutions on using Boosting to improve the Gaussian Naïve Bayes algorithm by combining Naïve Bayes classier and Adaboost methods. But despite these works, the Boosted Gaussian Naïve Bayes algorithm is still neglected in the resolution of classication problems. One of the reasons could be the complexity of the implementation of the algorithm compared to a standard Gaussian Naïve Bayes. We present in this paper, one approach of a suitable solution with a pseudo-algorithm that uses Boosting and Gaussian Naïve Bayes principles having the lowest possible complexity. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Journal articles on the topic 'Probabilistic scoring'

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