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Bayesian Heatmaps: Probabilistic Classification with Multiple Unreliable Information Sources
[article]
2019
arXiv
pre-print
Preserved Fulltext
We propose a novel Bayesian approach that models the relevance, error rates and bias of each information source, enabling us to learn a spatial Gaussian Process classifier by aggregating data from multiple ...
sources with varying reliability and relevance. ...
Our experiments demonstrated the advantages of integrating a confusion matrix model to capture the unreliability of different information sources with sharing information between sparse report locations ...
arXiv:1904.03063v1
fatcat:ygsulgdtz5c23j4ykdzm2b7hle
Bayesian Heatmaps: Probabilistic Classification with Multiple Unreliable Information Sources
[chapter]
2017
Lecture Notes in Computer Science
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We propose a novel Bayesian approach that models the relevance, error rates and bias of each information source, enabling us to learn a spatial Gaussian Process classifier by aggregating data from multiple ...
sources with varying reliability and relevance. ...
Our experiments demonstrated the advantages of integrating a confusion matrix model to capture the unreliability of different information sources with sharing information between sparse report locations ...
doi:10.1007/978-3-319-71246-8_7
fatcat:qskjzfhumbagta4zgvmwhryhy4
Modeling Annotation Uncertainty with Gaussian Heatmaps in Landmark Localization
[article]
2021
arXiv
pre-print
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2021 pre-print arXiv:2109.09533v1 fatcat:zwbllfxh4vc6nlyxjvcuydxpkq
Besides state-of-the-art results, our experiments on datasets of hand radiographs and lateral cephalograms also show that Gaussian functions are correlated with both localization accuracy and observer ...
As a final experiment, we show the importance of integrating the uncertainty into decision making by measuring the influence of the predicted location uncertainty on the classification of anatomical abnormalities ...
information in the heatmap is neglected. ...
arXiv:2109.09533v2
fatcat:w7uklee4ffdnhbtthkpjem3nca
Scaling Bayesian Probabilistic Record Linkage with Post-Hoc Blocking: An Application to the California Great Registers
[article]
2020
arXiv
pre-print
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Probabilistic record linkage (PRL) is the process of determining which records in two databases correspond to the same underlying entity in the absence of a unique identifier. ...
However, computational considerations severely limit the practical applicability of existing Bayesian approaches. ...
The rows and columns of the heatmaps correspond to records from file A and file B, respectively.Figure 1ashows a heatmap of the post-hoc blocking weights for each pair, with darker squares signifying larger ...
arXiv:1905.05337v2
fatcat:74isoqznijhsff32fkninwlhlq
An Uncertainty Estimation Framework for Probabilistic Object Detection
[article]
2021
arXiv
pre-print
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Robot actions based on high-confidence, yet unreliable predictions, may result in serious repercussions. ...
[8] propose a non-Bayesian alternative to BNNs by training an ensemble of multiple networks independently. ...
This information can then be used to augment the classification score and regressed bounding boxes, thus producing more reliable predictions. ...
arXiv:2106.15007v1
fatcat:rizbb5slfvbxlpley7e7x4c22q
BayesDB: A probabilistic programming system for querying the probable implications of data
[article]
2015
arXiv
pre-print
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for implementing BQL using a broad class of multivariate probabilistic models; (iii) a semi-parametric Bayesian model-builder that auomatically builds ensembles of factorial mixture models to serve as ...
This paper focuses on four aspects of BayesDB: (i) BQL, an SQL-like query language for Bayesian data analysis, that answers queries by averaging over an implicit space of probabilistic models; (ii) techniques ...
The authors are involved in an ongoing research partnership with the Bill and Melinda Gates Foundation aimed at integrating the Gapminder data with other relevant sources, including qualitative knowledge ...
arXiv:1512.05006v1
fatcat:grc2cak6djbelotuebzcdgejdm
Comprehensive aptamer-based screening identifies a spectrum of urinary biomarkers of lupus nephritis across ethnicities
2020
Nature Communications
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Most of these correlate significantly with disease activity indices in the respective ethnic groups, and surpass conventional metrics in identifying active LN, with better sensitivity, and negative/positive ...
For clustering, proteins were clustered in an unsupervised manner based on Euclidean distance with a maximum cluster size of 20. Random forest classification and Bayesian network analysis. ...
Further information on research design is available in the Nature Research Reporting Summary linked to this article.
Data availability Source data are provided as a Source Data files. ...
doi:10.1038/s41467-020-15986-3
pmid:32366845
fatcat:hth3a5iqd5fpphzrhp7szdapje
Evolution and emergence of multidrug-resistant Mycobacterium tuberculosis in Chişinău, Moldova
[article]
2021
medRxiv
pre-print
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Methods: Using whole genome sequence data and Bayesian phylogenomic methods, we reconstruct the stepwise acquisition of drug-resistance mutations in the MDR Ural/4.2 strain, estimate its historical bacterial ...
SNP differences calculated using all SNPs, including those associated with drug resistance) were considered clustered; and (2) a probabilistic approach that incorporates the SNP differences and information ...
Selected mutations associated with antimicrobial resistance (specifically those associated with resistance to isoniazid, streptomycin, rifampin, and fluoroquinolones) are annotated in the adjacent heatmap ...
doi:10.1101/2021.02.04.21251152
fatcat:qmfpwpc6nzdcvlgex3hsffygfy
Contextual Integration in Cortical and Convolutional Neural Networks
2020
Frontiers in Computational Neuroscience
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Our framework can potentially be applied to networks trained on other tasks, with the learned lateral connections aiding computations implemented by feedforward connections when the input is unreliable ...
It has been suggested that neurons can represent sensory input using probability distributions and neural circuits can perform probabilistic inference. ...
Many probabilistic models of cortical processing have multiple features at each location that contribute to generating an image patch, but not all of them require probabilities to sum to one (for eg, sparse ...
doi:10.3389/fncom.2020.00031
pmid:32390818
pmcid:PMC7192314
fatcat:w73zjz7invbulaehk6ck47kd3u
An Overview of Computational Approaches for Interpretation Analysis
[article]
2019
arXiv
pre-print
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2018 pre-print arXiv:1811.04028v1 fatcat:sjqnlinobnbitbxadxoullp2li
With this paper, in order to connect these approaches we introduce a theoretical framework for analyzing interpretation, which is applicable to interpretation of both human beings and computer models. ...
An early example of such a probabilistic model for combining human perspectives is the Dawid-Skene model [28] , which unites observations from different sources while estimating the observers' errors. ...
Saliency maps are simple to calculate for neural networks by means of backpropagation [117] , but on the downside, resulting heatmaps have been shown to be unreliable in certain cases [57] . ...
arXiv:1811.04028v2
fatcat:tsgpijxkyrgufnaahba2r77gb4
6. Interested Learning
[chapter]
2020
Engines of Order
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This chapter also develops the idea that contemporary information ordering represents an epistemological practice that can be described and analyzed as 'interested reading of reality', a particular kind ...
line with other information scientists of the time, as slow, unreliable, and biased (Maron, 1961) . ...
Second, since it constitutes a technique that is probabilistic (classifications are not binary but with degrees of certainty), adaptive (it 'learns' from experience), and well suited for personalization ...
doi:10.1515/9789048537419-008
fatcat:kmirv4p5pfbqldti3wemg7bni4
Probabilistic multivariate early warning signals
[article]
2022
arXiv
pre-print
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Here, we demonstrate that a probabilistic data aggregation strategy can provide new ways to improve early warning detection by more efficiently utilizing the available information from multivariate time ...
Critical transitions can be unexpected, with potentially catastrophic consequences. ...
Code availability Source code for the experiments is available at 10.5281/zenodo.6472720 ...
arXiv:2205.07576v1
fatcat:qe5x4ijb55dtxb4wqvjsr3asba
MITRE: inferring features from microbiota time-series data linked to host status
2019
Genome Biology
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Acknowledgements We thank Daniel DiGiulio for assistance with the data from reference [7] and Travis Gibson for helpful comments on the manuscript. ...
Competing interests GKG is a Strategic Advisory Board Member of Kaleido Biosciences and had a sponsored research agreement with the company, and is a Scientific Advisory Board Member, co-founder, and shareholder ...
Although ARM has some commonalities with Bayesian rule learning approaches, ARM methods tend to employ user-based cutoffs and heuristics, rather than principled probabilistic methods, as their primary ...
doi:10.1186/s13059-019-1788-y
pmid:31477162
pmcid:PMC6721208
fatcat:j4fkvup62zd73i5pwhx7s45ote
Predicting Blood–Brain Barrier Permeability of Marine-Derived Kinase Inhibitors Using Ensemble Classifiers Reveals Potential Hits for Neurodegenerative Disorders
2019
Marine Drugs
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We evaluated several regression and classification models, and found that our optimised classifiers (random forest, gradient boosting, and logistic regression) outperformed other models, with overall cross-validated ...
All 3 binary classifiers predicted 13 marine-derived kinase inhibitors with appropriate physicochemical characteristics for BBB permeability. ...
Bayesian ridge regression (BAYESRG) estimates a probabilistic model of RIDGE as described above, where priors over α, λ, ω are estimated during the model fitness. ...
doi:10.3390/md17020081
fatcat:lbpebeo6qzeevizc2ufwd4qt5y
Deep Learning Architectures for Amortized Bayesian Inference in Cognitive Modeling
2021
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We demonstrate how to perform inference on data sets with di erent sizes and probabilistic structure by using specialized network architectures which preserve the probabilistic symmetry of the target Bayesian ...
Broadly speaking, whenever an assumed mechanism transforms information into behavior, it is referred to as a cognitive process. ...
ADDITIONAL INFORMATION AND DECLARATIONS ...
doi:10.11588/heidok.00030807
fatcat:6vdp3u3buzec3pc32napfche5u
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