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89 results for “Bayesian estimation”
Development of a Bayesian Estimator for Calculating Plasma Iohexol Clearance
ClinicalTrials.gov study NCT05136963. IPD Sharing: NO. Countries: 1. Publications: 0.
Improving a Bayesian Model's Survival Estimates in Patients Needing Surgery for Bone Metastases
ClinicalTrials.gov study NCT01470105. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Data from: Bayesian estimation of fossil phylogenies and the evolution of early to middle Paleozoic crinoids (Echinodermata)
Open the record for dataset details and reuse information.
Data from: Bayesian estimation of genomic clines
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Data from: Improved estimation of macroevolutionary rates from fossil data using a Bayesian framework
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Improved RNA stability estimation through Bayesian modeling reveals most Salmonella transcripts have subminute half-lives
GEO Series GSE234010. Salmonella enterica subsp. enterica serovar Typhimurium. 132 samples. Type: Other; Expression profiling by high throughput sequencing.
Fig. 2 in A Systematist's Guide to Estimating Bayesian Phylogenies From Morphological Data
Fig. 2. Bayes theorem. Panel 'a' shows all the terms of Bayes' theorem. (a) is read 'the probability of the model given the data', and refers to the posterior probability. (b) is the likelihood, and is read 'the probability of the data given the model'. (c) is the prior probability of the model. (d) is the marginal probability of the data. Panel 'b' shows the same equation, but with which terms are model assumptions and which terms are observed data annotated.
An Integrated Approach to Battery Health Monitoring using Bayesian Regression, Classification and State Estimation
The application of the Bayesian theory of managing uncertainty and complexity to regression and classification in the form of Relevance Vector Machine (RVM), and to state estimation via Particle Filters (PF), proves to be a powerful tool to integrate the diagnosis and prognosis of battery health. Accurate estimates of the state-of-charge (SOC), the state-of-health (SOH) and state-of- life (SOL) for batteries provide a significant value addition to the management of any operation involving electrical systems. This is especially true for aerospace systems, where unanticipated battery performance may lead to catastrophic failures. Batteries, composed of multiple electro- chemical cells, are complex systems whose internal state variables are either inaccessible to sensors or hard to measure under operational conditions. In addition, battery performance is strongly influenced by ambient environmental and load conditions. Consequently, inference and estimation techniques need to be applied on indirect measurements, anticipated operational conditions and historical data, for which a Bayesian statistical approach is suitable. Accurate models of electro-chemical processes in the form of equivalent electric circuit parameters need to be combined with statistical models of state transitions, aging processes and measurement fidelity, need to be combined in a formal framework to make the approach viable. The RVM, which is a Bayesian treatment of the Support Vector Machine (SVM), is used for diagnosis as well as for model development. The PF framework uses this model and statistical estimates of the noise in the system and anticipated operational conditions to provide estimates of SOC, SOH and SOL. Validation of this approach on experimental data from Li-ion batteries is presented.
A Bayesian Framework for Remaining Useful Life Estimation
The estimation of remaining useful life (RUL) of a faulty component is at the center of system prognostics and health management. It gives operators a potent tool in decision making by quantifying how much time is left until functionality is lost. This is especially true for aerospace systems, where unanticipated subsystem downtime may lead to catastrophic failures. RUL prediction needs to contend with multiple sources of error like modeling inconsistencies, system noise and degraded sensor fidelity. Bayesian theory of uncertainty management provides a way to contain these problems by integrating out the nuisance variables. We use the Relevance Vector Machine (RVM), for model development. RVM is a Bayesian treatment of the well known Support Vector Machine (SVM), a kernel-based regression/classification technique. This model is next used in a Particle Filter (PF) framework. Statistical estimates of the noise in the system and anticipated operational conditions are processed to provide estimates of RUL in the form of a probability density function (PDF). Validation of this approach on experimental data collected from Li-ion batteries is presented.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.