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1,066 results for “bayesian”
Standard Bouguer anomaly model achieved by multi-source Bouguer gravity anomaly Bayesian data fusion algorithm in Sichuan-Yunnan region
<p>* Method: Based on the equivalent source inversion and Bayesian uncertainty quantization theory, a new multi-source gravity data fusion algorithm is developed, which effectively solves the multi-source data fusion problem with different noise and datum.</p> <p>* Standard Bouguer anomaly is Fused from WGM2012 Bouguer gravity anomaly model and 394 gravity profile data measured in Sichuan-Yunnan region. Fusion anomaly results can eliminate datum draft between multi-source gravity and reduce incoherent noise.</p> <p>* Spatial resolution of the standard Bouguer anomaly is about 20 kilometers.</p> <p>* Correcting deviations means the difference between the fused standard Bouguer anomaly model and the WGM2012 Earth gravity model.</p>
All simulation results, figures and code regarding the manuscript: Calibrating models of cancer invasion: parameter estimation using Approximate Bayesian Computation and gradient matching
<p>We present two different methods to estimate parameters within a partial differential equation (PDE) model of cancer invasion. The model describes the spatio-temporal evolution of three variables -- tumour cell density, extracellular matrix density and matrix degrading enzyme concentration -- in a one-dimensional tissue domain. The first method is a likelihood-free approach associated with Approximate Bayesian Computation (ABC); the second is a two-stage gradient matching method based on smoothing the data with a Generalized Additive Model (GAM) and matching gradients from the GAM to those from the model. Both methods performed well on simulated data. To increase realism, additionally we tested the gradient matching scheme with simulated measurement error and found that the ability to estimate some model parameters deteriorated rapidly as measurement error increased.</p>
Data from: Refining trophic dynamics through multi-factor Bayesian mixing models: a case study of subterranean beetles.
<p>Food web dynamics are vital in shaping the functional ecology of ecosystems. However, trophic ecology is still in its infancy in groundwater ecosystems due to the cryptic nature of these environments. To unravel trophic interactions between subterranean biota, we applied an interdisciplinary Bayesian mixing model design (multi-factor BMM) based on the integration of faunal C and N bulk tissue stable isotope data (δ<sup>13</sup>C and δ<sup>15</sup>N) with radiocarbon data (Δ<sup>14</sup>C), and prior information from metagenomic analyses. We further compared outcomes from multi-factor BMM with a conventional isotope double proxy mixing model (SIA BMM), triple proxy (δ<sup>13</sup>C, δ<sup>15</sup>N and Δ<sup>14</sup>C, multi-proxy BMM) and double proxy combined with DNA prior information (SIA+DNA BMM) designs. Three species of subterranean beetles (<i>Paroster macrosturtensis</i>, <i>Paroster mesosturtensis</i> and <i>Paroster microsturtensis</i>) and their main prey items Chiltoniidae<i> </i>amphipods (AM1: <i>Scutachiltonia axfordi</i> and AM2: <i>Yilgarniella sturtensis</i>), cyclopoids and harpacticoids from a calcrete in Western Australia were targeted. Diet estimations from stable isotope only models indicated homogeneous patterns with modest preferences for amphipods as prey items. Multi-proxy BMM suggested increased - and species-specific - predatory pressures on amphipods coupled with high rates of scavenging/predation on sister species. SIA+DNA BMM showed marked preferences for amphipods AM1 and AM2 and reduced interspecific scavenging/predation on <i>Paroster </i>species. Multi-factorial BMM revealed the most precise estimations (lower overall SD and very marginal beetles' interspecific interactions), indicating consistent preferences for amphipods AM1 in all the beetles' diets. Incorporation of genetic priors allowed crucial refining of the feeding preferences, while integration of more expensive radiocarbon data as a third proxy (when combined with genetic data) produced more precise outcomes but close dietary reconstruction to that from SIA+DNA BMM. Further multidisciplinary modelling from other groundwater environments will help elucidate the potential behind these designs and bring light to the feeding ecology of one the most vital ecosystems worldwide.</p>
An Introduction to a Bayesian Analysis of the Laboratory Origin of SARS-CoV-2
<p>An Introduction to a Bayesian Analysis of the Laboratory Origin of SARS-CoV-2</p>
Data from: Critically evaluating the theory and performance of Bayesian analyis of macroevolutionary mixtures
Bayesian analysis of macroevolutionary mixtures (BAMM) has recently taken the study of lineage diversification by storm. BAMM estimates the diversification-rate parameters (speciation and extinction) for every branch of a study phylogeny and infers the number and location of diversification-rate shifts across branches of a tree. Our evaluation of BAMM reveals two major theoretical errors: (i) the likelihood function (which estimates the model parameters from the data) is incorrect, and (ii) the compound Poisson process prior model (which describes the prior distribution of diversification-rate shifts across branches) is incoherent. Using simulation, we demonstrate that these theoretical issues cause statistical pathologies; posterior estimates of the number of diversification-rate shifts are strongly influenced by the assumed prior, and estimates of diversification-rate parameters are unreliable. Moreover, the inability to correctly compute the likelihood or to correctly specify the prior for rate-variable trees precludes the use of Bayesian approaches for testing hypotheses regarding the number and location of diversification-rate shifts using BAMM.
Data from: Bayesian quantification of ecological determinants of outcrossing in natural plant populations: computer simulations and the case study of biparental inbreeding in English yew
The mating system is a central parameter of plant biology because it shapes their ecological and evolutionary properties. Therefore, determining ecological variables that influence the mating system is important for a deeper understanding of the functioning of plant populations. Here, using old concepts and recent statistical developments, we propose a new statistical tool to make inferences about ecological determinants of outcrossing in natural plant populations. The method requires co-dominant genotypes of seeds collected from maternal plants within different locations. Using extensive computer simulations, we demonstrated that the method is robust to the issues expected for real-world data, including the Wahlund effect, inbreeding and genotyping errors such as allele dropout and allele misclassification. Furthermore, we showed that the estimates of ecological effects and outcrossing rates can be severely biased if genotyping errors and genetic differentiation are not treated explicitly. Application of the new method to the case study of a dioecious tree (Taxus baccata) allowed revealing that female trees that grow in lower local densities have a greater tendency towards mating with relatives. Moreover, we also demonstrated that biparental inbreeding is higher in populations that are characterised by a longer mean distance between trees and a smaller mean trunk perimeter. We found these results to agree with both the theoretical predictions and the history of English yew.
Data from: Disentangling elevational richness: a multi-scale hierarchical Bayesian occupancy model of Colorado ant communities
Understanding the forces that shape the distribution of biodiversity across spatial scales is central in ecology and critical to effective conservation. To assess effects of possible richness drivers, we sampled ant communities on four elevational transects across two mountain ranges in Colorado, USA, with seven or eight sites on each transect and twenty repeatedly sampled pitfall trap pairs at each site each for a total of 90 days. With a multi-scale hierarchical Bayesian community occupancy model, we simultaneously evaluated the effects of temperature, productivity, area, habitat diversity, vegetation structure, and temperature variability on ant richness at two spatial scales, quantifying detection error and genus-level phylogenetic effects. We fit the model with data from one mountain range and tested predictive ability with data from the other mountain range. In total, we detected 105 ant species, and richness peaked at intermediate elevations on each transect. Species-specific thermal preferences drove richness at each elevation with marginal effects of site-scale productivity. Trap-scale richness was primarily influenced by elevation-scale variables along with a negative impact of canopy cover. Soil diversity had a marginal negative effect while daily temperature variation had a marginal positive effect. We detected no impact of area, land cover diversity, trap-scale productivity, or tree density. While phylogenetic relationships among genera had little influence, congeners tended to respond similarly. The hierarchical model, trained on data from the first mountain range, predicted the trends on the second mountain range better than multiple regression, reducing root mean squared error up to 65%. Compared to a more standard approach, this modeling framework better predicts patterns on a novel mountain range and provides a nuanced, detailed evaluation of ant communities at two spatial scales.
Data from: A Bayesian approach for inferring the impact of a discrete character on rates of continuous-character evolution in the presence of background-rate variation
Understanding how and why rates of character evolution vary across the Tree of Life is central to many evolutionary questions; e.g., does the trophic apparatus (a set of continuous characters) evolve at a higher rate in fish lineages that dwell in reef versus non-reef habitats (a discrete character)? Existing approaches for inferring the relationship between a discrete character and rates of continuous-character evolution rely on comparing a null model (in which rates of continuous-character evolution are constant across lineages) to an alternative model (in which rates of continuous-character evolution depend on the state of the discrete character under consideration). However, these approaches are susceptible to a "straw-man" effect: the influence of the discrete character is inflated because the null model is extremely unrealistic. Here, we describe MuSSCRat, a Bayesian approach for inferring the impact of a discrete trait on rates of continuous-character evolution in the presence of alternative sources of rate variation ("background-rate variation"). We demonstrate by simulation that our method is able to reliably infer the degree of state-dependent rate variation, and show that ignoring background-rate variation leads to biased inferences regarding the degree of state-dependent rate variation in grunts (the fish group Haemulidae).
Informing antenna design for Global 21-cm experiments using a simulated Bayesian data analysis pipeline (supplementary data)
<p>These are the posterior files, foreground simulation data sets and chromaticity factor values used to produce the results for <a href="https://arxiv.org/abs/2106.10193">arXiv:2106.10193</a>.</p> <p>Plots of the fitted signal and residuals for each case are included, as is a plotting function to reproduce key figures.</p> <p>Naming conventions:</p> <ul> <li>f0: Centre frequency of the 21cm signal present in the simulated data</li> <li>A: Amplitude of the simulated 21cm signal present in the simulated data</li> <li>M_sig: Model being fit to the data includes a 21cm signal</li> <li>M_nosig: Model being fit to the data is a foreground only</li> </ul> <p>Software used:</p> <ul> <li><a href="https://github.com/PolyChord/PolyChordLite/tree/839292290a7747dbee82933bb9f7f955ac45c3ca">PolyChord</a></li> </ul> <p> </p>
Data from: Bayesian estimation of the global biogeographical history of the Solanaceae
Aim: The tomato family Solanaceae is distributed on all major continents except Antarctica and has its centre of diversity in South America. Its worldwide distribution suggests multiple long-distance dispersals within and between the New and Old Worlds. Here, we apply maximum likelihood (ML) methods and newly developed biogeographical stochastic mapping (BSM) to infer the ancestral range of the family and to estimate the frequency of dispersal and vicariance events resulting in its present-day distribution. Location: Worldwide. Methods: Building on a recently inferred megaphylogeny of Solanaceae, we conducted ML model fitting of a range of biogeographical models with the program 'BioGeoBEARS'. We used the parameters from the best fitting model to estimate ancestral range probabilities and conduct stochastic mapping, from which we estimated the number and type of biogeographical events. Results: Our best model supported South America as the ancestral area for the Solanaceae and its major clades. The BSM analyses showed that dispersal events, particularly range expansions, are the principal mode by which members of the family have spread beyond South America. Main conclusions: For Solanaceae, South America is not only the family's current centre of diversity but also its ancestral range, and dispersal was the principal driver of range evolution. The most common dispersal patterns involved range expansions from South America into North and Central America, while dispersal in the reverse direction was less common. This directionality may be due to the early build-up of species richness in South America, resulting in large pool of potential migrants. These results demonstrate the utility of BSM not only for estimating ancestral ranges but also in inferring the frequency, direction and timing of biogeographical events in a statistically rigorous framework.
River dams and the stability of bird communities: A hierarchical Bayesian analysis in a tropical hydroelectric power plant
<ol> <li>The effects of anthropogenic disturbance upon the stability of wildlife communities depend on the heterogeneity and connectivity of habitat remnants on multiple scales. The number of hydroelectric dams in biodiversity hotspots (Africa, South America and Asia) is growing rapidly. To establish their environmental impact, it is essential to understand the dynamics of wildlife communities before and following the establishment of dams.</li> <li>We evaluated the impacts of the filling of the Serra do Facão hydroelectric reservoir in the São Marcos river, central Brazil, upon the bird community. Using data from 1,145 surveys across 20 sampling sites over eight years, two years before and six years after the filling of the reservoir, we assessed the resistance, i.e., maintenance close to an equilibrium state during the disturbance, and resilience, i.e., ability to return to the original state following the disturbance, of the bird community. We used spatiotemporal hierarchical Bayesian models to assess the effects of reservoir filling on five community parameters: abundance, richness, phylogenetic diversity, functional diversity and species composition.</li> <li>In the period subsequent to reservoir filling, there was (i) a marked reduction in bird abundance, richness, phylogenetic diversity and functional diversity, and (ii) a reduction in the proportion of forest species, coupled with an increase in the proportion of savanna species. Except for bird abundance, none of the other community attributes returned to their original levels, even after six years. Our findings indicate that Cerrado bird communities have both low resistance and low resilience to habitat loss associated with the establishment of hydroelectric reservoirs.</li> <li> <i>Synthesis and applications.</i> The environmental costs of hydroelectric dams are still underestimated or neglected in Brazil. A new paradigm in the assessment of their environmental impacts is warranted, incorporating (i) models of spatiotemporal variations based on long-term monitoring with surveys initiated before disturbances and (ii) measures of functional and phylogenetic diversity, such that society can understand the costs and benefits of the establishment of new hydroelectric dams and make informed decisions. Biodiversity loss could be minimized by ensuring the preservation and connectivity of alluvial habitats, capable of maintaining the supply of resources and the functional and phylogenetic attributes of bird communities associated with such habitats.</li> </ol>
Bayesian inference of ancestral host-parasite interactions under a phylogenetic model of host repertoire evolution
<p>Intimate ecological interactions, such as those between parasites and their hosts, may persist over long time spans, coupling the evolutionary histories of the lineages involved. Most methods that reconstruct the coevolutionary history of such interactions make the simplifying assumption that parasites have a single host. Many methods also focus on congruence between host and parasite phylogenies, using cospeciation as the null model. However, there is an increasing body of evidence suggesting that the host ranges of parasites are more complex: that host ranges often include more than one host and evolve via gains and losses of hosts rather than through cospeciation alone. Here, we develop a Bayesian approach for inferring coevolutionary history based on a model accommodating these complexities. Specifically, a parasite is assumed to have a host repertoire, which includes both potential hosts and one or more actual hosts. Over time, potential hosts can be added or lost, and potential hosts can develop into actual hosts or vice versa. Thus, host colonization is modeled as a two-step process that may potentially be influenced by host relatedness. We first explore the statistical behavior of our model by simulating evolution of host-parasite interactions under a range of parameter values. We then use our approach, implemented in the program RevBayes, to infer the coevolutionary history between 34 Nymphalini butterfly species and 25 angiosperm families. Our analysis suggests that host relatedness among angiosperm families influences how easily Nymphalini lineages gain new hosts.</p>
A Bayesian Approach to Detect Pedestrian Destination-Sequences from WiFi Signatures: Data (tech. report 2013)
<p>This dataset contains the data used in:</p> <p>Danalet, A., Farooq, B. and Bierlaire, M. (2013). A Bayesian Approach to Detect Pedestrian Destination-Sequences from WiFi Signatures, Technical report, Transport and Mobility Laboratory, ENAC, Ecole Polytechnique Fédérale de Lausanne, Lausanne. URL: http://infoscience.epfl.ch/record/189759 (full text available)</p> <p>It contains data and a technical report describing</p> <ul> <li>WiFi traces</li> <li>Pedestrian Semantically-Enriched Routing Graph (SERG), and</li> <li>Potential Attractivity measure (PAM).</li> </ul>
Benchmark of the FRETR Bayesian restraint
<p>This directory contains all the scripts to run the benchmark of the FRET<sub>R</sub> Bayesian restraint, and the reference results.</p> <p>For more information about how to reproduce this modeling, see https://salilab.org/fret_benchmark or the README file.</p>
Figure 2. - Phylogenetic relationships among Dicronocephalus species reconstructed with Bayesian inference using COI sequences. Numbers above branches indicate ML bootstrap values and Bayesian posterior probabilities. Numbers below branches are bootstrap, symmetric resampling, and jacknife support from parsimony searches, respectively. Scale bar represents 10% nucleotide mutation rate.
Figure 2. - Phylogenetic relationships among Dicronocephalus species reconstructed with Bayesian inference using COI sequences. Numbers above branches indicate ML bootstrap values and Bayesian posterior probabilities. Numbers below branches are bootstrap, symmetric resampling, and jacknife support from parsimony searches, respectively. Scale bar represents 10% nucleotide mutation rate.
Figure 2. - Bayesian (GTR+Γ+I and HKY+Γ models) and maximum likelihood 50% majority-rule consensus tree. Numbers in the nodes represent posterior probabilities (GTR+Γ+I and HKY+Γ, respectively), and bootstrap value for maximum likelihood and parsimony analyses, respectively. c1–Bragança, Pará; c2–Santa Maria do Pará, Pará; c3–National Forest of Amapá, Amapá; c4–Belém, Pará; i1–Solimões River, near Manaus, Amazonas; i2–Xingu River, Altamira, Pará; i3 and i4–Itacoatiara, Amazonas. MYBP–million years before present.
Figure 2. - Bayesian (GTR+Γ+I and HKY+Γ models) and maximum likelihood 50% majority-rule consensus tree. Numbers in the nodes represent posterior probabilities (GTR+Γ+I and HKY+Γ, respectively), and bootstrap value for maximum likelihood and parsimony analyses, respectively. c1–Bragança, Pará; c2–Santa Maria do Pará, Pará; c3–National Forest of Amapá, Amapá; c4–Belém, Pará; i1–Solimões River, near Manaus, Amazonas; i2–Xingu River, Altamira, Pará; i3 and i4–Itacoatiara, Amazonas. MYBP–million years before present.
Figure 4. - Phylogenetic relationships among Dicronocephalus species reconstructed with Bayesian inference using COI and 16S rRNA sequences. Numbers above branches indicate ML bootstrap values and Bayesian posterior probabilities. Numbers below branches are bootstrap, symmetric resampling, and jacknife support from parsimony searches, respectively. Scale bar represents 10% nucleotide mutation rate.
Figure 4. - Phylogenetic relationships among Dicronocephalus species reconstructed with Bayesian inference using COI and 16S rRNA sequences. Numbers above branches indicate ML bootstrap values and Bayesian posterior probabilities. Numbers below branches are bootstrap, symmetric resampling, and jacknife support from parsimony searches, respectively. Scale bar represents 10% nucleotide mutation rate.
Figure 3. - Phylogenetic relationships among Dicronocephalus species reconstructed with Bayesian inference using 16S rRNA sequences. Numbers above branches indicate ML bootstrap values and Bayesian posterior probabilities. Numbers below branches are bootstrap, symmetric resampling, and jacknife support from parsimony searches, respectively. Scale bar represents 10% nucleotide mutation rate.
Figure 3. - Phylogenetic relationships among Dicronocephalus species reconstructed with Bayesian inference using 16S rRNA sequences. Numbers above branches indicate ML bootstrap values and Bayesian posterior probabilities. Numbers below branches are bootstrap, symmetric resampling, and jacknife support from parsimony searches, respectively. Scale bar represents 10% nucleotide mutation rate.
Figure 1. - Bayesian phylogeny of Euptychia based on one mitochondrial (COI) and one nuclear (EF1-a) gene. Posterior probabilities are listed above and bootstrap values below branches. A dash denotes bootstrap support lower than 50%. (Euptychiaattenboroughi is not included in the analysis – see text for details.)
Figure 1. - Bayesian phylogeny of Euptychia based on one mitochondrial (COI) and one nuclear (EF1-a) gene. Posterior probabilities are listed above and bootstrap values below branches. A dash denotes bootstrap support lower than 50%. (Euptychiaattenboroughi is not included in the analysis – see text for details.)
Data associated with "Robust Structured Illumination Microscopy with Bayesian Noise Control"
<p>Experimental and synthetic data saved in tiff and/or zarr formats and SIM reconstruction scripts written in Python (Wiener and FISTA-SIM0. These also include estimated SIM patterns which are needed for B-SIM</p><ul><li>Synthetic data consisting of variably spaced line pairs. Found in <a href="https://zenodo.org/uploads/10037823">2023_10_02_synthetic_line_pairs.zip</a></li><li>Experimental data. Fluorescence images of one of the variably spaced line pair patterns on an ArgoSIM calibration slide. Found in <a href="https://zenodo.org/uploads/10037823">2023_08_02_folder=002_argosim_slide.zip</a></li><li>Experimental data. MitoTracker Red labelled mitochondria in live HeLa cells. Found in <a href="2023_08_07_folder=011_mitos_live_hela.zip">2023_08_07_folder=011_mitos_live_hela.zip</a></li><li>Camera calibration maps, including gain, variance, and offset. Found in <a href="camera_calibration.zip">camera_calibration.zip</a></li></ul>
ScienceDex guides
Understand access before you commit
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.