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About 10,000 papers for “Graphical model”

Graphical Model

David Kellen, Dries Trippas, Henrik Singmann · OSF Preprints (OSF Preprints) · 2018

Cited by 0

Graphical Model

Henrik Singmann, Dries Trippas, David Kellen · OSF Preprints (OSF Preprints) · 2018

Cited by 0

Graphical model.

Florencia Sabbione (4862455), Irene Angelica Keitelman (17692572), Carolina Maiumi Shiromizu (11754866) et al. · Figshare · 2023

<p><b><a href="https://openclipart.org/detail/36289/scissors" target="_blank">https://openclipart.org/detail/36289/scissors</a></b>. Challenge of neutrophils with STEC induces ROS production dependent on NADPH oxidase activation. ROS would trigger elastase release from azurophil granules to the cytosol which could be able to process both Gasdermin D (GSDMD) and pro-IL-1β. Likewise, STEC also trigger inflammasome activation and consequently caspase-1 activation which is able to also process both GSDMD and pro-IL-1β. The N-terminal fragment of GSDMD can form pores on azurophil granule membranes increasing elastase leakage to the cytosol. The amount of active cytosolic elastase would determine if pro-IL-1β is processed to the mature IL-1β isoform or degraded.</p>

Cited by 0Open access

Graphical Model

Dries Trippas, David Kellen, Henrik Singmann · OSF Preprints (OSF Preprints) · 2018

Cited by 0

Graphical model.

Javier Kipen (17350750), Joakim Jaldén (17350753) · Figshare · 2023

<p>This graphical model represents the dependencies between the states, observations and the randomly picked peptide distribution in our model.</p>

Cited by 0Open access

Graphical magnet model

Salvermoser, T · CERN Document Server (European Organization for Nuclear Research) · 1996

Cited by 0

Graphical Model Sketch

Branislav Kveton, Hung Bui, Mohammad Ghavamzadeh et al. · arXiv (Cornell University) · 2016

Structured high-cardinality data arises in many domains, and poses a major challenge for both modeling and inference. Graphical models are a popular approach to modeling structured data but they are unsuitable for high-cardinality variables. The count-min (CM) sketch is a popular approach to estimating probabilities in high-cardinality data but it does not scale well beyond a few variables. In this work, we bring together the ideas of graphical models and count sketches; and propose and analyze several approaches to estimating probabilities in structured high-cardinality streams of data. The key idea of our approximations is to use the structure of a graphical model and approximately estimate its factors by "sketches", which hash high-cardinality variables using random projections. Our approximations are computationally efficient and their space complexity is independent of the cardinality of variables. Our error bounds are multiplicative and significantly improve upon those of the CM sketch, a state-of-the-art approach to estimating probabilities in streams. We evaluate our approximations on synthetic and real-world problems, and report an order of magnitude improvements over the CM sketch.

Cited by 1Open access

Graphical model summary.

Audrey McCombs (7068065), Claus Kadelka (3815755) · Figshare · 2020

<p>(A) Example of the two-layer interaction network used in this study. The private, small-world network (red edges) is shown on top of the public, fully-connected network (gray edges). Low-risk (blue circles) and high-risk (black squares) individuals are distinguished. (B) Illustration of the stochastic transmission model with compartments S = susceptible, E = exposed, A = asymptomatic, I = symptomatic, H = hospitalized, R = recovered, D = deceased. Individuals in I and H may receive a positive test (green “tested” oval). Edges that are influenced by policy decisions are colored: red = social distancing, green = testing, blue = hospital triage. Branching probabilities at E, I and H are risk-group dependent and the edge of the respectively more likely transition is thicker. (C) Illustration of the time-dependent transmission rate of an exposed individual increasing until peak viral shedding, which coincides with transition to compartment I (if symptomatic) or A (otherwise). (D) Average care per person (blue solid line) and total care provided (red dashed line) by a health care system with a capacity threshold of 100% operating at a certain level of (over)capacity. Once the capacity threshold is reached, the average care per person is .</p>

Cited by 0Open access

Model / Graphical Abstract.

Nikhila S. Tanneti (8374965), Joel D. Federspiel (3368378), Ileana M. Cristea (246789) et al. · Figshare · 2020

<p><b>A:</b> Neuronal Maturation is required for efficient and robust anterograde spread of virus. Immature neurons lack the proteome necessary to regulate spread of virus particles, thus both Us9<sup>WT</sup> and the spread deficient Us9<sup>YY</sup> PRV particles can sort. Neuronal maturation is accompanied with establishing an Axon and expression of proteins specialized to regulate anterograde spread. <b>B:</b> SMPD4 blocks anterograde spread. Capsids (red) that assemble into membranes containing Us9 (green) can recruit transport machinery, such as the Kif1a microtubule motor, to facilitate anterograde spread along the axon. Capsids that colocalizing with SMPD4 foci (blue) do not assemble into Us9 membranes and thus fail to recruit transport machinery necessary for anterograde spread.</p>

Cited by 0Open access

Graphical Model Sketch

Branislav Kveton, Hung Bui, Mohammad Ghavamzadeh et al. · Lecture notes in computer science · 2016

Cited by 5

The graphical model

Xiang Zhang (19800), Wei Cheng (52808), Jennifer Listgarten (80389) et al. · Figshare · 2015

<p>Known and potential TFs are assumed to be mutually independent. Regulated genes are assumed to be mutually independent given the TFs.</p>

Cited by 0Open access

Graphical model check.

Nina Singer (6532766), Ludwig Kreuzpointner (480303), Monika Sommer (6532769) et al. · Figshare · 2019

<p>Graphical model check plotting the item values estimated for participants with EMCS scores less or equal than the median against the item values estimated for participants with EMCS scores above the median. The results indicated a proper fit for all 40 items.</p>

Cited by 0Open access

Mixed graphical model.

Lotte P. Brinkhof (11701112), K. Richard Ridderinkhof (11701121), Sanne de Wit (547329) et al. · Figshare · 2025

<b>Panel A:</b> Green lines (i.e., edges) indicate positive relationships, while red lines indicate negative relationships. Grey edges indicate pairwise interactions involving categorical variables. The exact nature of these interactions are described in text. The width of all lines is proportional to the absolute value of the strength of the relationship. Solid lines depict relationships that were non-zero more than 90% of the bootstrap samples (i.e., very stable), and dashed lines depict relationships that were non-zero between 85% and 90% of the bootstraps (i.e., relatively stable). (Relatively) unstable relationships (non-zero < 85%) are not shown to improve visibility, but can be found in Table 1 of <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0315445#pone.0315445.s002" target="_blank">S2 Appendix</a>. The black rings around the Gaussian variables indicate the proportion of variance explained (R<sup>2</sup>, i.e., node-wise predictability) by neighboring nodes. The grey rings around the categorical variables indicate the accuracy. In <b>panel B</b>, Gaussian variables are ordered according to their node-wise predictability. The numbers in the right column indicate the number of edges that are connected to each node (including unstable edges that are not shown in the figure). The six nodes that were most central are shown in bold.

Cited by 0Open access

Graphical State Space Model

Shaolin Lü · arXiv (Cornell University) · 2021

In this paper, a new framework, named as graphical state space model, is proposed for the real time optimal estimation of a class of nonlinear state space model. By discretizing this kind of system model as an equation which can not be solved by Extended Kalman filter, factor graph optimization can outperform Extended Kalman filter in some cases. A simple nonlinear example is given to demonstrate the efficiency of this framework.

Cited by 0Open access