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130 results for “Markov”
Figure 9. Experimental Page Rank dependency on Markov Chain length with balanced distribution-Study of a Random Navigation on the Web Using Software Simulation
<p>This paper explored different implementation choices for analyzing the most important<br> parameters about a web. Many researchers explored the use of new search engines for studying the<br> evolution of the web (Ntoulas, Cho and Olston, 2004). Another important research is realized about<br> the Link Structure Graph (LSG). The LSG captures a complete hyperlink structure from the web<br> and models link associations reflected in the page layout (Rodrigues, Milic-Frayling and Fortuna,<br> 2007). For further works ideas like extrapolation methods for accelerating page rank calculation can<br> be developed (Kamvar et al., 2003).</p>
Figure 7. Experimental Page Rank dependency on Markov Chain length-Study of a Random Navigation on the Web Using Software Simulation
<p>The next diagram proves that the values for Experimental Page Rank depend on the length<br> of the Markov Chain, while Algorithmic Page Rank remains constant.</p>
Figure 1. Markov Chain Model&Figure 2. Transition matrix-Study of a Random Navigation on the Web Using Software Simulation
<p>For a good simulation it is very important to find methods for<br> navigating through the web (Levene and Wheeldon, 2004). John Kemeny and Laurie Snell have<br> proposed the use of Markov models for web simulations (Kemeny and Snell, 1960). Cadez et al. (2000)<br> used Markov models for classifying the sessions into different categories for browsers. Some other<br> proposed techniques choose to combine different order Markov models for obtaining low state<br> complexity and improving accuracy, as Deshpande and Karypis (2004). Dongshan and Junyi (2002)<br> used for predicting the access providing good scalability and high coverage a hybrid-order tree-like<br> Markov model. As an alternative to the Markov model Pitkow proposed a longest subsequence model<br> (Pitkow and Pirolli, 1999), also for predicting the next page accessed by the user Sarukkai chose<br> Markov models (Sarukkai, 2000).<br> Transitions are simulated using the Markov Chain nodes, Google matrix and an arbitrary initial<br> probability distribution. Examples can be seen in Figure 1 and Figure 2.</p>
Monthly NAO data for GRL paper "Understanding the Signal-to-noise Paradox with a Simple Markov Model"
<p>The datasets include monthly NAO data for GRL paper "Understanding the Signal-to-noise Paradox with a Simple Markov Model". The monthly NAO index is estimated based on the leading empirical orthogonal function mode of the Mean Sea Level Pressure (SLP) over the North Atlantic. The monthly NAO index has been normalized for each dataset seperately. The description of each file is shown as follows: </p> <ul> <li>monthly_nao_cmip5_models_1871_2005.nc: monthly NAO index derived from 40 CMIP5 model outputs (historical run; first realization)</li> <li>cmip5_model_list: list of 40 CMIP5 models</li> <li> monthly_nao_era20c_1900_2005.nc: monthly NAO index derived from the ECMWF's twentieth-century reanalysis data (ERA20C)</li> <li>monthly_nao_noaa20c_1871_2005.nc: monthly NAO index derived from the NOAA's twentieth-century reanalysis version-2 data (NOAA20C) </li> </ul>
A new sampling capability for uncertainty quantification in the Ice-sheet and Sea-level System Model v4.19 using Gaussian Markov random fields -- Datasets and results
<p>Data archives for test experiments (Section 3) and Pine Island Glacier application (Section 4) from the manuscript "Kevin Bulthuis and Eric Larour, A new sampling capability for uncertainty quantification in the Ice-sheet and Sea-Level System Model v4.19 using Gaussian Markov random fields"</p> <p>Source code is available at https://doi.org/10.5281/zenodo.5532775.</p>
Timing data for algorithms for calculating steady state distributions of continuous time Markov chains
<p>Data showing the timings for a number of algorithms for the computation of the steady state of a continuous time Markov chain:</p> <ul> <li>Numeric integration;</li> <li>Matrix exponential;</li> <li>Eigenvector;</li> <li>Solve a linear algebraic system;</li> <li>Approximately solving a linear algebraic system.</li> </ul> <p>All calculations were done using implementations in Python for a blog post at https://vknight.org/blog/</p>
Accompanying empirical data for Kirchherr et al., 2023, "Bayesian multilevel hidden Markov models identify stable state dynamics in longitudinal recordings from macaque primary motor cortex"
<p>This repository contains data accompanying: Kirchherr et al., 2023, "Bayesian multilevel hidden Markov models identify stable state dynamics in longitudinal recordings from macaque primary motor cortex".</p> <p>Data collection methods:</p> <p>Two adult female rhesus macaques (Macaca mulatta) trained on a reaching, and grasping, and placing task served as the subjects. The animal handling as well as surgical and experimental procedures complied with European guideline (2010/63/UE) and authorized by the French Ministry for Higher Education and Research (project # 2016112713202878) in force on the care and use of laboratory animals, and were approved by the ethics committee CELYNE (comité d’éthique Lyonnais pour les neurosciences expérimentale, C2EA 42). After initial training, we performed a sterile surgery to implant six floating multielectrode arrays (FMA, Microprobes for Life Science, Gaithersburg, MD, USA) in the right (monkey 1) or left (monkey 2) cortical hemisphere. Each array was comprised of 32 platinum/iridium electrodes (impedance 0.5 MΩ at 1 kHz) with lengths ranging from 1 to 6 mm, and with an inter-electrode spacing of 400 μm. One electrode array was implanted in the primary motor cortex (M1), two were implanted in the ventral premotor cortex (F5), one in the dorsal premotor cortex (F2), and two in the prefrontal cortex (45a and 46/12r), as estimated according to a previous magnetic resonance imaging scan. For the purposes of this study, we analyzed data from the M1 array of each monkey.</p> <p>The wideband neural signal (bandpass filtered at 0.1 to 7500 kHz) was recorded at 30 kS/s, and amplified and digitized (16-bit; 0.192 μV resolution) with an Intan Tech-based (Intan Technologies, Los Angeles, CA, USA) open source acquisition system (Open Ephys; Siegle et al. 2017). This system uses a 256-channel Intan RHD2000 series acquisition board and 32-channel headstages (RHD2132). Spike detection was performed offline using Trisdesclous (Garcia & Pouzat,2015). The common reference was removed to reduce ambient noise. Spikes were then detected from each electrode using a threshold of 2 times the median absolute deviation (MAD), and analyzed as multi-unit activity (MUA) in 10 ms bins. All electrodes in which at least one well-isolated spike waveform was detected were selected for the following analyses. We thus used a sample of 21 electrodes out of 32 for monkey 1, and 25 out of 32 electrodes for monkey 2. Custom made detection panels were used to record the moments when the monkey’s hand released the handle, the hand contacted the target object, and when the object was placed in the groove. An Omniplex 16-channel recording system (Plexon, Dallas, TX, USA) was used to simultaneously record these behavioral events. Trials were discarded if the response time (time between the go signal and handle release) was less than 100 or greater than 1500 ms, the reach duration (time between handle release and object contact) was less than 100 or greater than 1000 ms, or the placing duration (time between object contact and placing the object in the groove) was less than 100 or greater than 1200 ms, leaving 19 - 68 trials per day for monkey 1 (M = 43.9, SD = 15.46, N = 439; left: M = 14.8, SD = 5.74; center: M = 14.4, SD = 5.15; right: M = 14.7, SD = 7.73), and 23 - 49 per day for monkey 2 (M = 38.3, SD = 9.87, N = 383; left: M = 14.2, SD = 3.91; center: M = 10.8, SD = 3.55; right: M = 13.3, SD = 3.37).</p> <p><br> Abstract:</p> <p>Neural populations, rather than single neurons, may be the fundamental unit of cortical computation. Analyzing chronically recorded neural population activity is challenging not only because of the high dimensionality of activity in many neurons, but also because of changes in the recorded signal that may or may not be due to neural plasticity. Hidden Markov models (HMMs) are a promising technique for analyzing such data in terms of discrete, latent states, but previous approaches have either not considered the statistical properties of neural spiking data, have not been adaptable to longitudinal data, or have not modeled condition specific differences. We present a multilevel Bayesian HMM which addresses these shortcomings by incorporating multivariate Poisson log-normal emission probability distributions, multilevel parameter estimation, and trial-specific condition covariates. We applied this framework to multi-unit neural spiking data recorded using chronically implanted multi-electrode arrays from macaque primary motor cortex during a cued reaching, grasping, and placing task. We show that the model identifies latent neural population states which are tightly linked to behavioral events, despite the model being trained without any information about event timing. We show that these events represent specific spatiotemporal patterns of neural population activity and that their relationship to behavior is consistent over days of recording. The utility and stability of this approach is demonstrated using a previously learned task, but this multilevel Bayesian HMM framework would be especially suited for future studies of long-term plasticity in neural populations.</p>
Data from: Properties of Markov chain Monte Carlo performance across many empirical alignments --part II
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Data from: Properties of Markov chain Monte Carlo performance across many empirical alignments -- part I
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Estimating waiting distances between genealogy changes under a multi-species extension of the sequentially Markov coalescent
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Data from: Forest tree breeding using genomic Markov causal models: A new approach to genomic tree breeding improvement
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A Bayesian Phylogenetic Hidden Markov Model for B Cell Receptor Sequence Analysis
<p>simulation and PC64/VRC01 input/output data files</p>
From pup to predator; generalized hidden Markov models reveal rapid development of movement strategies in a naïve long‐lived vertebrate
<p>Rapid development of a successful foraging strategy is critical for juvenile survival, especially for naïve animals that receive no parental guidance. However, this process is poorly understood for many species. Although observation of early-life movements is increasingly possible with miniaturisation of animalborne telemetry devices, analytical limitations remain. Here, we tracked 29 recently-weaned, grey seal <em>Halichoerus grypus</em> pups from colonies in two geographically distinct regions of the United Kingdom. We analysed at-sea movements of pups throughout their initial months of nutritional independence to investigate the ontogeny of behaviour-specific (foraging and travelling) movement patterns. Using generalized hidden Markov models (HMMs), we extended the conventional HMM framework to account for temporal changes in putative foraging and travelling movement characteristics, and investigate the effects of intrinsic (sex) and extrinsic (environment) factors on this process. Putative foraging behaviour became more tortuous with time, and travelling became faster and more directed, suggesting a reduction in search scale and an increase in travel efficiency as pups shifted from exploration to an adult-like repeatable foraging strategy. Sex differences in movement characteristics were evident from colony departure, but sex-specific activity budgets were only detected in one region. We show that sex-specific behavioural strategies emerge before sexual size dimorphism in grey seals, and suggest that this phenomenon may occur in other long-lived species. Our results also indicate that environmental variation may affect the emergence of sex-specific foraging behaviour, highlighting the need to consider interacting intrinsic and extrinsic factors in shaping movement strategies of long-lived vertebrates. Moreover, comparing the behavioural state estimations to those of a conventional HMM (no variation in statespecific movement parameters) revealed differences in the amount and location of foraging activity, with implications for spatial conservation management. Overlooking intrinsic and extrinsic variation in movement processes could distort our understanding of foraging ecology, population dynamics, and conservation requirements.</p>
Supporting data: Structure-based Markov random field model for representing evolutionary constraints on functional sites
<p>Supporting data for the paper:</p> <p>Chan-Seok Jeong, Dongsup Kim. Structure-based Markov random field model for representing evolutionary constraints on functional sites. Submitted. (2015)</p> <p>See 'README' for a description of the contents.</p>
Plasim dataset for manuscript titled: "Extreme heatwave sampling and prediction with analog Markov chain and comparisons with deep learning"
<p>This dataset was originally generated by running a Plasim general circulation model for 8000 years. We extract a small subset of this dataset which is only 500 years long with temperature, soil moisture and 500 hPa Geopotential extracted over the northern hemisphere above 30 North. This dataset can be used to reproduce the results in https://arxiv.org/abs/2307.09060 concerning training CNN and SWG with cross-validation length of 500 years. </p>
Multi-parameter photon-by-photon hidden Markov modeling dataset
<p>The core jupyter notebooks demonstrating mpH<sup>2</sup>MM using real nsALEX data on DNA hairpin, the maltose binding protein MalE, and the type III secretion system effector YopO. Also included are the jupyter notebooks for generating simulated photon trajectories to test the validity of the Integrated complete likelihood (ICL) and mpH<sup>2</sup>MM on data where the ground truth is known.</p> <p>Included are all HDF5 files used by the jupyter notebooks. Those for MalE and YopO are contained in zip files, and should be unziped maintaining the directory structure. All other HDF5 files should be kept in the same folder as the jupyter notebooks. Additional figures and csv files of H2MM models are included in separate zip file.</p>
Predicting past and future SARS-CoV-2-related sick leave using discrete time Markov modelling
<p><strong>Background: </strong>Prediction of SARS-CoV-2-induced sick leave among healthcare workers (HCWs) is essential for being able to plan the healthcare response to the epidemic.</p> <p><strong>Methods: </strong>During first wave of the SARS-Cov-2 epidemic (April 23<sup>rd </sup>to June 24<sup>th</sup>, 2020), the HCWs in the greater Stockholm region in Sweden were invited to a study of past or present SARS-CoV-2 infection. We develop a discrete time Markov model using a cohort of 9449 healthcare workers (HCWs) who had complete data on SARS-CoV-2 RNA and antibodies as well as sick leave data for the calendar year 2020. The one-week and standardized longer term transition probabilities of sick leave and the ratios of the standardized probabilities for the baseline covariate distribution were compared with the referent period (an independent period when there were no SARS-CoV-2 infections) in relation to PCR results, serology results and gender.</p> <p><strong>Results:</strong> The one-week probabilities of transitioning from healthy to partial sick leave or full sick leave during the outbreak as compared to after the outbreak were highest for healthy HCWs testing positive for large amounts of virus (ratio: 3.69, (95% confidence interval, CI: 2.44-5.59) and 6.67 (95% CI: 1.58-28.13), respectively). The proportion of all sick leaves attributed to COVID-19 during outbreak was at most 55% (95% CI: 50%-59%).</p> <p><strong>Conclusions: </strong>A robust Markov model enabled use of simple SARS-CoV-2 testing data for quantifying past and future COVID-related sick leave among HCWs, which can serve as a basis for planning of healthcare during outbreaks.</p>
Do Go Chasing Waterfalls: Enoyl Reductase (FabI) in Complex with Inhibitors Stabilizes the Tetrameric Structure and Opens Water Channels - trajectories employed in Markov State Models and water analyses
<p>The following trajectories were employed in the generation of MSM models and water analyses:</p> <p>SaFabI_60us_align.zip</p> <p>EcFabI_60us_align.zip</p> <p>waters_SaFabI.tar.gz</p> <p>waters_EcFabI.tar.gz</p>
Figure 8. Exchange mutation.-Neuroevolution Mechanism for Hidden Markov Model
<p>Exchange two neighbor weights involved in a summation of 1.0. This is illustrated in Figure 8.</p>
Unsupervised detection of Large-scale Weather Patterns in the Northern Hemisphere via Markov State Modelling: from Blockings to Teleconnections
<p><em><span>Data Source</span></em></p> <p>This dataset is derived from the <em><span>NCEP-NCAR Reanalysis 1</span> data provided by the NOAA PSL, Boulder, Colorado, USA, from their website at <a href="https://psl.noaa.gov/">https://psl.noaa.gov</a></em>., a robust atmospheric dataset that includes a wide range of climatic measurements essential for comprehensive climate analysis. The original data can be accessed at the NOAA Physical Sciences Laboratory website: https://psl.noaa.gov/data/gridded/data.ncep.reanalysis.html.</p> <p> </p>
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.