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Bayesian Heatmaps: Probabilistic Classification with Multiple Unreliable Information Sources [article]

Edwin Simpson, Steven Reece, Stephen J. Roberts
2019 arXiv   pre-print
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]

Edwin Simpson, Steven Reece, Stephen J. Roberts
2017 Lecture Notes in Computer Science  
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]

Franz Thaler, Christian Payer, Martin Urschler, Darko Stern
2021 arXiv   pre-print
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]

Brendan S. McVeigh, Bradley T. Spahn, Jared S. Murray
2020 arXiv   pre-print
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]

Zongyao Lyu, Nolan B. Gutierrez, William J. Beksi
2021 arXiv   pre-print
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]

Vikash Mansinghka, Richard Tibbetts, Jay Baxter, Pat Shafto, Baxter Eaves
2015 arXiv   pre-print
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

Samantha Stanley, Kamala Vanarsa, Samar Soliman, Deena Habazi, Claudia Pedroza, Gabriel Gidley, Ting Zhang, Shree Mohan, Evan Der, Hemant Suryawanshi, Thomas Tuschl, Jill Buyon (+5 others)
2020 Nature Communications  
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]

Tyler S Brown, Vegard Eldholm, Ola Brynildsrud, Magnus Osnes, Natalie Stennis, James Stimson, Caroline Colijn, Sofia Alexandru, Ecaterina Noroc, Nelly Ciobanu, Valeriu Crudu, Ted Cohen (+1 others)
2021 medRxiv   pre-print
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

Ramakrishnan Iyer, Brian Hu, Stefan Mihalas
2020 Frontiers in Computational Neuroscience  
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]

Philipp Blandfort, Jörn Hees, Desmond U. Patton
2019 arXiv   pre-print
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  
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]

Ville Laitinen, Leo Lahti
2022 arXiv   pre-print
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

Elijah Bogart, Richard Creswell, Georg K. Gerber
2019 Genome Biology  
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

Fabien Plisson, Andrew Piggott
2019 Marine Drugs  
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

Stefan Radev
2021
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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