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61 results for “Bayesian method”
Accompanying dataset for: "A Bayesian method for predicting background radiation at environmental monitoring stations"
<h3>Physical parameters included</h3> <ul> <li>Ten-minute-averaged ambient dose equivalent rates (nSv/h) observed by the Immission Monitors for Ring area (IMR stations) at the sites of the SCK CEN and the Doel NPP for selected periods in time</li> </ul> <h3>Geographic locations</h3> <ul> <li>Belgian Nuclear Research Centre (SCK CEN) in Mol, Belgium: 18 IMR stations</li> <li>Nuclear Power Plant in Doel, Belgium: 16 IMR stations</li> </ul> <h3>Periods</h3> <ul> <li>6 through 13 August 2022</li> <li>30 August through 1 September 2022</li> <li>10 through 12 September 2022</li> </ul> <h3>Description of data</h3> <p>The zipped folder contains three sub folders for the different periods of interest. Each sub folder contains 34 files. Files are either named IMR-D##.txt to indicate Doel-based or IMR-M##.txt to indicate SCK CEN-based stations. Exact locations (WGS84) are included in the headers. Time stamps (referred to as 'local_time' in the files) are given in Central European Summer Time (UTC+2), and the ambient dose equivalent rates (referred to as 'value' in the files) in nanosievert per hour (nSv/h).</p> <h3>Acknowledgements</h3> <p>The authors thank François Menneson from FANC-ACFN for providing access to the Telerad data.</p>
Accompanying software for: "A Bayesian method for predicting background radiation at environmental monitoring stations"
<h3>Introduction</h3> <p>This software accompanies: "A Bayesian Method for predicting background radiation at environmental monitoring stations". The software is written in Python and depends (besides on standard packages like numpy) on the PyMC package for Bayesian inference. A brief user manual is provided that will allow to install the necessary prerequisites in a conda environment, and describes how to perform the inferences that are described in the paper. This requires additionally downloading the dataset that we have also made available on this platform (<a href="https://doi.org/10.5281/zenodo.12581795" target="_blank" rel="noopener">10.5281/zenodo.12581795</a>). </p> <h3>Description of files</h3> <ul> <li><em>manual.pdf</em> describes how to install the necessary packages in conda, and how to perform inferences from the paper.</li> <li><em>main.py</em> is the main script, which contains the input parameters and calls the relevant functions.</li> <li><em>bayesian_inference.py</em> contains the beating heart of the software. Here, the Bayesian problems for calibration and predictions are set up and solved using the PyMC package.</li> <li><em>data_paper_interface.py </em>is only necessary when reproducing the data from the paper. It contains the different cases that were used in the paper, and can be used to parse data from the accompanying dataset.</li> </ul>
Data from: A new method of Bayesian causal inference in non-stationary environments
Bayesian inference is the process of narrowing down the hypotheses (causes) to the one that best explains the observational data (effects). To accurately estimate a cause, a considerable amount of data is required to be observed for as long as possible. However, the object of inference is not always constant. In this case, a method such as exponential moving average (EMA) with a discounting rate is used to improve the ability to respond to a sudden change; it is also necessary to increase the discounting rate. That is, a trade-off is established in which the followability is improved by increasing the discounting rate, but the accuracy is reduced. Here, we propose an extended Bayesian inference (EBI), wherein human-like causal inference is incorporated. We show that both the learning and forgetting effects are introduced into Bayesian inference by incorporating the causal inference. We evaluate the estimation performance of the EBI through the learning task of a dynamically changing Gaussian mixture model. In the evaluation, the EBI performance is compared with those of the EMA and a sequential discounting expectation-maximization algorithm. The EBI was shown to modify the trade-off observed in the EMA.
Data from: A novel Bayesian method for inferring and interpreting the dynamics of adaptive landscapes from phylogenetic comparative data
Our understanding of macroevolutionary patterns of adaptive evolution has greatly increased with the advent of large-scale phylogenetic comparative methods. Widely used Ornstein-Uhlenbeck (OU) models can describe an adaptive process of divergence and selection. However, inference of the dynamics of adaptive landscapes from comparative data is complicated by interpretational difficulties, lack of identifiability among parameter values and the common requirement that adaptive hypotheses must be assigned a priori. Here we develop a reversible-jump Bayesian method of fitting multi-optima OU models to phylogenetic comparative data that estimates the placement and magnitude of adaptive shifts directly from the data. We show how biologically informed hypotheses can be tested against this inferred posterior of shift locations using Bayes Factors to establish whether our a priori models adequately describe the dynamics of adaptive peak shifts. Furthermore, we show how the inclusion of informative priors can be used to restrict models to biologically realistic parameter space and test particular biological interpretations of evolutionary models. We argue that Bayesian model-fitting of OU models to comparative data provides a framework for integrating of multiple sources of biological data–such as microevolutionary estimates of selection parameters and paleontological timeseries–allowing inference of adaptive landscape dynamics with explicit, process-based biological interpretations.
Data from: Bayesian methods outperform parsimony but at the expense of precision in the estimation of phylogeny from discrete morphological data
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Data from: Elevated substitution rate estimates from ancient DNA: model violation and bias of Bayesian methods
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Data from: The impact of variable degrees of freedom and scale parameters in Bayesian methods for genomic prediction in Chinese Simmental beef cattle
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Raw in vitro screening data and R scripts for: A Bayesian method for population-wide cardiotoxicity hazard and risk characterization using an in vitro human model
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Data from: A new Bayesian method for fitting evolutionary models to comparative data with intraspecific variation
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Data from: A Bayesian method for analyzing lateral gene transfer
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Data from: Implementing and testing Bayesian and Maximum likelihood supertree methods in phylogenetics
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Data from: A new method of Bayesian causal inference in non-stationary environments
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Data from: Bayesian methods for estimating GEBVs of threshold traits
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Data from: A novel Bayesian method for inferring and interpreting the dynamics of adaptive landscapes from phylogenetic comparative data
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Data from: Bayesian morphological clock methods resurrect placoderm monophyly and reveal rapid early evolution in jawed vertebrates
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A Bayesian method of evaluating discomfort due to glare: The effect of order bias from a large glare source
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Vancomycin Dose Adjustments Comparing Trough Levels to The AUC/MIC Method Using a Bayesian Approach in a Hospitalized Adult Population
ClinicalTrials.gov study NCT04756895. IPD Sharing: NO. Countries: 1. Publications: 0.
Bayesian Sequential Single Case Methods to Personalize Low-Intensity Psychological Interventions: Initial Pilot Work
ClinicalTrials.gov study NCT04779437. IPD Sharing: YES. Countries: 1. Publications: 0.
Probabilistic Delamination Diagnosis of Composite Materials Using a Novel Bayesian Imaging Method
In this paper, a probabilistic delamination location and size detection framework is proposed. The delamination probability image using Lamb wave-based damage detection is constructed using the Bayesian updating technique. First, the algorithm for the probabilistic delamination detection framework using Bayesian updating (Bayesian Imaging Method - BIM) is proposed. Following this, the composite coupon fatigue testing setup is introduced and the corresponding lamb wave diagnosis signal is collected and interpreted. Next, the obtained signal features are incorporated in the Bayesian Imaging Method to detect delamination size and location, as well as their confidence bounds. The damage detection results using the proposed methodology are compared with X-ray images for verification and validation. Finally, some conclusions and future works are drawn based on the proposed study.
Prognostics Methods for Battery Health Monitoring Using a Bayesian Framework
This paper explores how the remaining useful life (RUL) can be assessed for complex systems whose internal state variables are either inaccessible to sensors or hard to measure under operational conditions. Consequently, inference and esti- mation techniques need to be applied on indirect measurements, anticipated operational conditions, and historical data for which a Bayesian statistical approach is suitable. Models of electrochem- ical processes in the form of equivalent electric circuit parame- ters were combined with statistical models of state transitions, aging processes, and measurement fidelity in a formal frame- work. Relevance vector machines (RVMs) and several different particle filters (PFs) are examined for remaining life prediction and for providing uncertainty bounds. Results are shown on battery data.1 Index Terms—Battery health, Bayesian learning, particle filter, prognostics, relevance vector machine, remaining useful life.
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.