scholarly journals Bayesian approach to learn Bayesian networks using data and constraints

2016 ◽  
Author(s):  
Gao Xiao-guang ◽  
Yang Yu ◽  
Guo Zhi-gao ◽  
Chen Da-qing
Keyword(s):  
Bayesian Networks ◽  
Bayesian Approach ◽  
Using Data

Improving the Forecasting Process in Project Control

2014 ◽  
pp. 1173-1181
Author(s):  
Franco Caron
Keyword(s):  
Bayesian Approach ◽  
Future Development ◽  
Control Process ◽  
Knowledge Sources ◽  
Project Control ◽  
Forecasting Accuracy ◽  
Feed Forward Control ◽  
Using Data

In the project control process, the role of the ETC (Estimate to Complete) is critical, since, given a feed forward control loop, the only way to influence the overall project performance is to take actions affecting the work remaining. The forecasting accuracy related to ETC is linked to the ability of the project team to exploit all the knowledge available in order to anticipate the future development of the project. According to the classification of the knowledge sources it is possible to identify three different approaches to determining the ETC: (1) using data records related to the work completed by highlighting possible trends (2) adjusting the trend stemming from data records using experts' judgment and (3) integrating the internal view of the project, i.e. data records related to the work completed and experts' judgment related to work remaining, with data records deriving from similar projects completed in the past. A Bayesian approach may represent a possible way of integrating the different knowledge sources.

scholarly journals Bayesian approach to determining penetrance of pathogenic SDH variants

2018 ◽  
Vol 55 (11) ◽  
pp. 729-734 ◽  
Author(s):  
Diana E Benn ◽  
Ying Zhu ◽  
Katrina A Andrews ◽  
Mathilda Wilding ◽  
Emma L Duncan ◽  
...  
Keyword(s):  
Bayesian Approach ◽  
Genetic Variants ◽  
The Cancer Genome Atlas ◽  
Pathogenic Variants ◽  
Combining Data ◽  
Disease Penetrance ◽  
Cancer Genome Atlas ◽  
Observational Cohort ◽  
Using Data ◽  
Hereditary Pheochromocytoma

BackgroundUntil recently, determining penetrance required large observational cohort studies. Data from the Exome Aggregate Consortium (ExAC) allows a Bayesian approach to calculate penetrance, in that population frequencies of pathogenic germline variants should be inversely proportional to their penetrance for disease. We tested this hypothesis using data from two cohorts for succinate dehydrogenase subunits A, B and C (SDHA–C) genetic variants associated with hereditary pheochromocytoma/paraganglioma (PC/PGL).MethodsTwo cohorts were 575 unrelated Australian subjects and 1240 unrelated UK subjects, respectively, with PC/PGL in whom genetic testing had been performed. Penetrance of pathogenic SDHA–C variants was calculated by comparing allelic frequencies in cases versus controls from ExAC (removing those variants contributed by The Cancer Genome Atlas).ResultsPathogenic SDHA–C variants were identified in 106 subjects (18.4%) in cohort 1 and 317 subjects (25.6%) in cohort 2. Of 94 different pathogenic variants from both cohorts (seven in SDHA, 75 in SDHB and 12 in SDHC), 13 are reported in ExAC (two in SDHA, nine in SDHB and two in SDHC) accounting for 21% of subjects with SDHA–C variants. Combining data from both cohorts, estimated lifetime disease penetrance was 22.0% (95% CI 15.2% to 30.9%) for SDHB variants, 8.3% (95% CI 3.5% to 18.5%) for SDHC variants and 1.7% (95% CI 0.8% to 3.8%) for SDHA variants.ConclusionPathogenic variants in SDHB are more penetrant than those in SDHC and SDHA. Our findings have important implications for counselling and surveillance of subjects carrying these pathogenic variants.

Efficiency measurement of research groups using Data Envelopment Analysis and Bayesian networks

2009 ◽  
Vol 83 (3) ◽  
pp. 711-721 ◽  
Author(s):  
Cristhian Fabián Ruiz ◽  
Ricardo Bonilla ◽  
Diego Chavarro ◽  
Luis Antonio Orozco ◽  
Roberto Zarama ◽  
...  
Keyword(s):  
Data Envelopment Analysis ◽  
Bayesian Networks ◽  
Efficiency Measurement ◽  
Data Envelopment ◽  
Research Groups ◽  
Using Data

Comparing Interval Restricted Estimators in Hedonic Pricing / Ein Vergleich intervallrestringierter Schätzverfahren in der hedonischen Preismessung

2000 ◽  
Vol 220 (5) ◽  
Author(s):  
Henning Knautz
Keyword(s):  
Monte Carlo ◽  
Bayesian Approach ◽  
Housing Prices ◽  
Hedonic Pricing ◽  
Monte Carlo Experiment ◽  
Constrained Estimation ◽  
Pricing Models ◽  
Interval Constraints ◽  
Using Data ◽  
Fuzzy Interval

SummaryIn hedonic pricing models there is often prior knowledge available which has the form of interval constraints on the unknown coefficients. These are stemming for example from considerations of submarkets for the characteristics involved. In this article we briefly discuss some well known estimators that allow for incorporation of this knowledge. Additionally we introduce two new promising approaches for the same purpose: a modified Bayesian approach and a method applying fuzzy interval constraints. Using data on housing prices we present the results of a Monte Carlo experiment in which these estimators are compared. It turns out that constrained estimation is promising especially in the situation of high multicollinearity and moderate R2 which is typical for hedonic pricing models. We illustrate that estimates and confidence intervals for the unknown coefficients can be improved substantially compared with the conventional unrestricted estimation.

scholarly journals Bayesian mapping of the striatal microcircuit reveals robust asymmetries in the probabilities and distances of connections

2021 ◽  
Author(s):  
Francois Cinotti ◽  
Mark D Humphries
Keyword(s):  
Bayesian Approach ◽  
D2 Receptor ◽  
Cognitive Disorders ◽  
Projection Neuron ◽  
Current Data ◽  
Projection Neurons ◽  
Range Of Movement ◽  
Precise Knowledge ◽  
Wide Range ◽  
Using Data

The striatum's complex microcircuit is made by connections within and between its D1- and D2-receptor expressing projection neurons and at least five species of interneuron. Precise knowledge of this circuit is likely essential to understanding striatum's functional roles and its dysfunction in a wide range of movement and cognitive disorders. We introduce here a Bayesian approach to mapping neuron connectivity using intracellular recording data, which lets us simultaneously evaluate the probability of connection between neuron types, the strength of evidence for it, and its dependence on distance. Using it to synthesise a complete map of the rodent striatum, we find strong evidence for two asymmetries: a selective asymmetry of projection neuron connections, with D2 neurons connecting twice as densely to other projection neurons than do D1 neurons, but neither subtype preferentially connecting to another; and a length-scale asymmetry, with interneuron connection probabilities remaining non-negligible at more than twice the distance of projection neuron connections. We further show our Bayesian approach can evaluate evidence for wiring changes, using data from the developing striatum and a mouse model of Huntington's disease. By quantifying the uncertainty in our knowledge of the microcircuit, our approach reveals a wide range of potential striatal wiring diagrams consistent with current data.

Bayesianism: Objections and Rebuttals

2021 ◽  
pp. 267-286
Author(s):  
Norman Fenton ◽  
David Lagnado
Keyword(s):  
Bayesian Networks ◽  
Bayesian Network ◽  
Bayesian Approach ◽  
Legal Reasoning ◽  
Statistical Evidence ◽  
Subjective Measure ◽  
Probability Judgments ◽  
Legal Case ◽  
The World ◽  
The Bayesian Approach

While the laws of probability are rarely disputed, the question of how we should interpret probability judgments is less straightforward. Broadly, there are two ways to conceive of probability—either as an objective feature of the world, or as a subjective measure of our uncertainty. Both notions have their place in science, but it is the latter subjective notion (the Bayesian approach) that is crucial in legal reasoning. This chapter explains the advantages of using Bayesian networks in adjudicative factfinding. It addresses a number of common objections to the Bayesian approach, such as “There is no such thing as a probability of a single specified event”; “The Bayesian approach only works with statistical evidence”; “The Bayesian approach is too difficult for legal factfinders to comprehend”; and “A Bayesian network can never capture the full complexity of a legal case.” Fenton and Lagnado offer rebuttals to each of these objections.

Data fusion by joint non-negative matrix factorization for hypothesizing pseudo-chemistry using Bayesian networks

2020 ◽  
Vol 5 (9) ◽  
pp. 1719-1737
Author(s):  
Anjana Puliyanda ◽  
Kaushik Sivaramakrishnan ◽  
Zukui Li ◽  
Arno de Klerk ◽  
Vinay Prasad
Keyword(s):  
Data Fusion ◽  
Bayesian Networks ◽  
Matrix Factorization ◽  
Reaction Networks ◽  
Spectroscopic Sensors ◽  
Using Data ◽  
Non Negative Matrix Factorization

We infer reaction networks and chemistry using data fusion of spectroscopic sensors.

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