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34 results for “Estimation Framework”
Data from: A quantitative framework to estimate the relative importance of environment, spatial variation and patch connectivity in driving community composition
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A multi-state occupancy modeling framework for robust estimation of disease prevalence in multi-tissue disease systems
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Data from: A statistical framework for neuroimaging data analysis based on mutual information estimated via a gaussian copula
We begin by reviewing the statistical framework of information theory as applicable to neuroimaging data analysis. A major factor hindering wider adoption of this framework in neuroimaging is the difficulty of estimating information theoretic quantities in practice. We present a novel estimation technique that combines the statistical theory of copulas with the closed form solution for the entropy of Gaussian variables. This results in a general, computationally efficient, flexible, and robust multivariate statistical framework that provides effect sizes on a common meaningful scale, allows for unified treatment of discrete, continuous, unidimensional and multidimensional variables, and enables direct comparisons of representations from behavioral and brain responses across any recording modality. We validate the use of this estimate as a statistical test within a neuroimaging context, considering both discrete stimulus classes and continuous stimulus features. We also present examples of analyses facilitated by these developments, including application of multivariate analyses to MEG planar magnetic field gradients, and pairwise temporal interactions in evoked EEG responses. We show the benefit of considering the instantaneous temporal derivative together with the raw values of M/EEG signals as a multivariate response, how we can separately quantify modulations of amplitude and direction for vector quantities, and how we can measure the emergence of novel information over time in evoked responses. Open-source Matlab and Python code implementing the new methods accompanies this article.
A Bayesian framework for estimating parameters of a generic toxicokinetic model for the bioaccumulation of organic chemicals by benthic invertebrates: proof of concept with PCB153 and two freshwater species.
<p>R Codes and corresponding .csv data files for estimating toxicokinetic model parameters for both chironomids and gammarids exposed to PCB153.</p>
Data from: A framework for estimating the fixation time of an advantageous allele in stepping-stone models
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Data from: Rarefaction and extrapolation with Hill numbers: a framework for sampling and estimation in species diversity studies
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Data from: A statistical framework for neuroimaging data analysis based on mutual information estimated via a gaussian copula
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Data from: An analytical framework for estimating fertilization bias and the fertilization set from multiple sperm-storage organs
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Data from: Improved estimation of macroevolutionary rates from fossil data using a Bayesian framework
The estimation of origination and extinction rates and their temporal variation is central to understanding diversity patterns and the evolutionary history of clades. The fossil record provides the only direct evidence of extinction and biodiversity changes through time and has long been used to infer the dynamics of diversity changes in deep time. The software PyRate implements a Bayesian framework to analyze fossil occurrence data to estimate the rates of preservation, origination and extinction while incorporating several sources of uncertainty. Building upon this framework, we present a suite of methodological advances including more complex and realistic models of preservation and the first likelihood-based test to compare the fit across different models. Further, we develop a new reversible jump Markov chain Monte Carlo algorithm to estimate origination and extinction rates and their temporal variation, which provides more reliable results and includes an explicit estimation of the number and temporal placement of statistically significant rate changes. Finally, we implement a new C++ library which speeds up the analyses by orders of magnitude, therefore facilitating the application of PyRate to large datasets. We demonstrate the new functionalities through extensive simulations and with the analysis of a large dataset of Cenozoic marine mammals. We compare our analytical framework against two alternative methods to infer origination and extinction rates revealing that PyRate decisively outperforms them across a range of simulated datasets. Our analyses indicate that explicit statistical model testing, which is often neglected in fossil-based macroevolutionary analyses, is crucial to obtain accurate and robust results.
A Trial for a Preliminary Estimate of the Effectiveness of a Novel Theoretical Framework
ClinicalTrials.gov study NCT06461416. IPD Sharing: NO. Countries: 1. Publications: 0.
Data from: A new framework for analysing automated acoustic species detection data: occupancy estimation and optimization of recordings post-processing
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Data from: Improved estimation of macroevolutionary rates from fossil data using a Bayesian framework
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RSPF-based Prognosis Framework for Estimation of Remaining Useful Life in Energy Storage Devices
This paper presents a case study where a RSPF-based prognosis framework is applied to estimate the remaining useful life of an energy storage device (Li-Ion battery). A comparison based on prognosis performance metrics indicates that the RSPF-based prognostic approach is more suitable than classic PF methods to represent rare events such as capacity regeneration phenomena between charging periods, in terms of accuracy of the state estimate and steadiness of the RUL estimate. We surmise that the existence of particles in the tails of the state pdf allow the RSPF-based prognostic algorithm to generally provide a more conservative estimate of the RUL of the faulty piece of equipment. We surmise that it also helps to incorporate the probability of rare and costly events in the evolution of the fault condition in time.
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.
ScienceDex guides
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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.