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209 results for “probabilistic”
Data from: How to measure maturation: a comparison of probabilistic methods used to test for genotypic variation and plasticity in the decision to mature
Maturation is a developmental trait that plays a key role in shaping organisms' life-history. However, progress in understanding how maturation phenotypes evolve has been held back by confusion over how best to model maturation decisions and a lack of studies comparing genotypic variation in maturation. Here, we fitted probabilistic maturation reaction norms (PMRNs) to data collected from five clones of Daphnia magna and five of Daphnia pulex collected from within and between different populations. We directly compared the utility of modeling approaches that assume maturation to be a process with an instantaneous rate with those that do not by fitting maturation rate and logistic regression models, and emphasize similarities and differences between them. Our results demonstrate that in Daphnia, PMRNs using a logistic regression approach were simpler to use and provided a better fit to the data. The decision to mature was plastic across a range of growth trajectories and dependent upon both body size and age. However, the age effect was stronger in D. magna than D. pulex and varied considerably between clones. Our results support the idea that maturation thresholds can evolve but also suggest that the notion of a threshold based on a single fixed state is an oversimplification that underestimates the adaptability of these important traits.
Data from: Probabilistic methods surpass parsimony when assessing clade support in phylogenetic analyses of discrete morphological data
Fossil taxa are critical to inferences of historical diversity and the origins of modern biodiversity, but realizing their evolutionary significance is contingent on restoring fossil species to their correct position within the tree of life. For most fossil species, morphology is the only source of data for phylogenetic inference; this has traditionally been analysed using parsimony, the predominance of which is currently challenged by the development of probabilistic models that achieve greater phylogenetic accuracy. Here, based on simulated and empirical datasets, we explore the relative efficacy of competing phylogenetic methods in terms of clade support. We characterize clade support using bootstrapping for parsimony and Maximum Likelihood, and intrinsic Bayesian posterior probabilities, collapsing branches that exhibit less than 50% support. Ignoring node support, Bayesian inference is the most accurate method in estimating the tree used to simulate the data. After assessing clade support, Bayesian and Maximum Likelihood exhibit comparable levels of accuracy, and parsimony remains the least accurate method. However, Maximum Likelihood is less precise than Bayesian phylogeny estimation, and Bayesian inference recaptures more correct nodes with higher support compared to all other methods, including Maximum Likelihood. We assess the effects of these findings on empirical phylogenies. Our results indicate probabilistic methods should be favoured over parsimony.
Data from: Probabilistic distances between trees
Most existing measures of distance between phylogenetic trees are based on the geometry or topology of the trees. Instead, we consider distance measures which are based on the underlying probability distributions on genetic sequence data induced by trees. Monte Carlo schemes are necessary to calculate these distances approximately, and we describe efficient sampling procedures. Key features of the distances are the ability to include substitution model parameters and to handle trees with different taxon sets in a principled way. We demonstrate some of the properties of these new distance measures and compare them to existing distances, in particular by applying multidimensional scaling to data sets previously reported as containing phylogenetic islands.
Data from: Neural structure mapping in human probabilistic reward learning
Humans can learn abstract concepts that describe invariances over relational patterns in data. One such concept, known as magnitude, allows stimuli to be compactly represented on a single dimension (i.e. on a mental line). Here, we measured representations of magnitude in humans by recording neural signals whilst they viewed symbolic numbers. During a subsequent reward-guided learning task, the neural patterns elicited by novel complex visual images reflected their payout probability in a way that suggested they were encoded onto the same mental number line, with 'bad' bandits sharing neural representation with 'small' numbers and 'good' bandits with 'large' numbers. Using neural network simulations, we provide a mechanistic model that explains our findings and shows how structural alignment can promote transfer learning. Our findings suggest that in humans, learning about reward probability is accompanied by structural alignment of value representations with neural codes for the abstract concept of magnitude.
Data from: Probabilistic graphical model representation in phylogenetics
Recent years have seen a rapid expansion of the model space explored in statistical phylogenetics, emphasizing the need for new approaches to statistical model representation and software development. Clear communication and representation of the chosen model is crucial for: (1) reproducibility of an analysis, (2) model development and (3) software design. Moreover, a unified, clear and understandable framework for model representation lowers the barrier for beginners and non-specialists to grasp complex phylogenetic models, including their assumptions and parameter/variable dependencies. Graphical modeling is a unifying framework that has gained in popularity in the statistical literature in recent years. The core idea is to break complex models into conditionally independent distributions. The strength lies in the comprehensibility, flexibility, and adaptability of this formalism, and the large body of computational work based on it. Graphical models are well-suited to teach statistical models, to facilitate communication among phylogeneticists and in the development of generic software for simulation and statistical inference. Here, we provide an introduction to graphical models for phylogeneticists and extend the standard graphical model representation to the realm of phylogenetics. We introduce a new graphical model component, tree plates, to capture the changing structure of the subgraph corresponding to a phylogenetic tree. We describe a range of phylogenetic models using the graphical model framework and introduce modules to simplify the representation of standard components in large and complex models. Phylogenetic model graphs can be readily used in simulation, maximum likelihood inference, and Bayesian inference using, for example, Metropolis-Hastings or Gibbs sampling of the posterior distribution.
Probabilistic Hierarchical Interpolation and Interpretable Configuration for Flood Prediction
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Probabilistic Emulation of the Community Radiative Transfer Model Using Machine Learning
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Probabilistic Alignment of Single-Cell Data
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Data from: The importance of social dimension and maturation stage for the probabilistic maturation reaction norm in Poecilia reticulata
Maturation is an important event in an organism's life history, with important implications on dynamics of both wild and captive populations. The probabilistic maturation reaction norm (PMRN) has emerged as an important method to describe variation in maturation in wild fish. Because most PMRNs are based on age and size only, it is important to understand limitations of these variables in explaining maturation. We experimentally assessed (i) the sensitivity of age- and size-based PMRNs to unaccounted sources of plasticity, (ii) the role of social environment on maturation and (iii) the significance of estimating PMRNs early and late in the maturation process (initiation and completion of maturation, respectively). We reared male guppies (Poecilia reticulata) under laboratory conditions, subjected to two food levels and three different social cues. We found that growth and social environment affected the maturation in a way that could not be accounted for by their effect on age and size. PMRNs estimated for the initiation stage were less plastic (growth differences and social cues influenced the PMRN shape only little) than those for completion. The initiation of maturation is probably closer to the maturation 'decision' and allows determining factors influencing maturation decision most accurately.
Data from: A probabilistic metric for the validation of computational models
A new validation metric is proposed that combines the use of a threshold based on the uncertainty in the measurement data with a normalised relative error, and that is robust in the presence of large variations in the data. The outcome from the metric is the probability that a model's predictions are representative of the real world based on the specific conditions and confidence level pertaining to the experiment from which the measurements were acquired. Relative error metrics are traditionally designed for use with series of data values but orthogonal decomposition has been employed to reduce the dimensionality of data matrices to feature vectors so that the metric can be applied to fields of data. Three previously published case studies are employed to demonstrate the efficacy of this quantitative approach to the validation process in the discipline of structural analysis, for which historical data was available; however, the concept could be applied to a wide range of disciplines and sectors where modelling and simulation plays a pivotal role.
Efficient Probabilistic Prediction and Uncertainty Quantification of Tropical Cyclone-driven Storm Tides and Inundation: Model Data and Analysis Code
<p>This repository contains model data and analysis codes related to the manuscript entitled "Efficient Probabilistic Prediction and Uncertainty Quantification of Tropical Cyclone-driven Storm Tides and Inundation", as follows:</p> <ol> <li>Model data are maximum water surface elevations of ensemble 48-hr forecast ADCIRC model simulations for three historical US landfalling hurricanes: 2017 Irma, 2018 Florence, and 2020 Laura. These are located in the "NameYYYY_Results.tar" archive files as "maxele.63.nc" files. Also included in the tar files are the hurricane forecast track files in Automated Tropical Cyclone Forecasting (ATCF) system format (*.22) and the error variable parameters (*.json) for each forecast. </li> <li>Model data of best-track runs for the 2017 Irma, 2018 Florence, and 2020 Laura hurricanes, and astronomical tide-only runs for the corresponding time periods are located in the "NameYYYY_besttrack+tides.tar" archive files. Both the maximum water surface elevations "maxele.63.nc" and the time series of water surface elevations "fort.63.nc" are included. </li> <li>ADCIRC input mesh (*.14) and mesh property files (*.13) are included in "ADCIRC_mesh_files.zip".</li> <li>Joint Karhunen-Loeve Polynomial Chaos (KL-PC) analysis python scripts with and without considering inundation are located in "klpc_analysis_scripts.zip". Requires <a href="https://github.com/noaa-ocs-modeling/EnsemblePerturbation">EnsemblePerturbation</a> python toolbox. </li> <li>Python scripts for analyzing and plotting the KL-PC results (Figures 6-14 and Table 1 in the manuscript) are located in "results_plotting_scripts.zip". Requires <a href="https://github.com/noaa-ocs-modeling/EnsemblePerturbation">EnsemblePerturbation</a> python toolbox. </li> </ol>
FMRI Study of Performance During a Probabilistic Reversal Learning Task in Depression
ClinicalTrials.gov study NCT00075296. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Data from: A probabilistic metric for the validation of computational models
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Data from: Integrating sequence evolution into probabilistic orthology analysis
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Data from: Probabilistic distances between trees
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Data from: Neural structure mapping in human probabilistic reward learning
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Data from: Probabilistic methods surpass parsimony when assessing clade support in phylogenetic analyses of discrete morphological data
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Data from: The importance of social dimension and maturation stage for the probabilistic maturation reaction norm in Poecilia reticulata
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Data from: Probabilistic methods outperform parsimony in the phylogenetic analysis of data simulated without a probabilistic model
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Data from: Probabilistic graphical model representation in phylogenetics
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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)
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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.