Scalable spatio‐temporal Bayesian analysis of high‐dimensional electroencephalography data

2021 ◽  
Vol 49 (1) ◽  
pp. 107-128
Author(s):  
Shariq Mohammed ◽  
Dipak K. Dey
Keyword(s):  
Bayesian Analysis ◽  
High Dimensional ◽  
Spatio Temporal

scholarly journals The Bayesian Analysis of Complex, High-Dimensional Models: Can It Be CODA?

2014 ◽  
Vol 29 (4) ◽  
pp. 619-639 ◽  
Author(s):  
Y. Ritov ◽  
P. J. Bickel ◽  
A. C. Gamst ◽  
B. J. K. Kleijn
Keyword(s):  
Bayesian Analysis ◽  
High Dimensional ◽  
Dimensional Models

scholarly journals Author Correction: Spatio-temporal patterns of childhood pneumonia in Bhutan: a Bayesian analysis

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Kinley Wangdi ◽  
Kinley Penjor ◽  
Tsheten Tsheten ◽  
Chachu Tshering ◽  
Peter Gething ◽  
...  
Keyword(s):  
Bayesian Analysis ◽  
Temporal Patterns ◽  
Childhood Pneumonia ◽  
Spatio Temporal

Optical reservoir computing for high-dimensional spatio-temporal chaotic systems prediction (Conference Presentation)

2020 ◽  
Author(s):  
Mushegh Rafayelyan ◽  
Jonathan Dong ◽  
Yongqi Tan ◽  
Florent Krzakala ◽  
Sylvain Gigan
Keyword(s):  
Chaotic Systems ◽  
High Dimensional ◽  
Reservoir Computing ◽  
Spatio Temporal

scholarly journals Spatio-temporal predictive modeling framework for infectious disease spread

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sashikumaar Ganesan ◽  
Deepak Subramani
Keyword(s):  
Predictive Modeling ◽  
Dimensional Space ◽  
Scalar Function ◽  
Disease Spread ◽  
High Dimensional ◽  
Modeling Framework ◽  
High Dimensional Space ◽  
Susceptible Population ◽  
Infected Population ◽  
Spatio Temporal

AbstractA novel predictive modeling framework for the spread of infectious diseases using high-dimensional partial differential equations is developed and implemented. A scalar function representing the infected population is defined on a high-dimensional space and its evolution over all the directions is described by a population balance equation (PBE). New infections are introduced among the susceptible population from a non-quarantined infected population based on their interaction, adherence to distancing norms, hygiene levels and any other societal interventions. Moreover, recovery, death, immunity and all aforementioned parameters are modeled on the high-dimensional space. To epitomize the capabilities and features of the above framework, prognostic estimates of Covid-19 spread using a six-dimensional (time, 2D space, infection severity, duration of infection, and population age) PBE is presented. Further, scenario analysis for different policy interventions and population behavior is presented, throwing more insights into the spatio-temporal spread of infections across duration of disease, infection severity and age of the population. These insights could be used for science-informed policy planning.

Semiparametric Bayesian analysis of high-dimensional censored outcome data

2017 ◽  
Vol 1 (2) ◽  
pp. 194-204
Author(s):  
Chetkar Jha ◽  
Yi Li ◽  
Subharup Guha
Keyword(s):  
Bayesian Analysis ◽  
Outcome Data ◽  
High Dimensional

scholarly journals Identifying temporal and spatial patterns of variation from multi-modal data using MEFISTO

2020 ◽  
Author(s):  
Britta Velten ◽  
Jana M. Braunger ◽  
Damien Arnol ◽  
Ricard Argelaguet ◽  
Oliver Stegle
Keyword(s):  
Factor Analysis ◽  
Dimensionality Reduction ◽  
Data Driven ◽  
High Dimensional ◽  
Modal Data ◽  
Temporal And Spatial Patterns ◽  
Spatially Resolved ◽  
Temporal And Spatial ◽  
Spatio Temporal ◽  
Personalized Health

AbstractFactor analysis is among the most-widely used methods for dimensionality reduction in genome biology, with applications from personalized health to single-cell studies. Existing implementations of factor analysis assume independence of the observed samples, an assumption that fails in emerging spatio-temporal profiling studies. Here, we present MEFISTO, a flexible and versatile toolbox for modelling high-dimensional data when spatial or temporal dependencies between the samples are known. MEFISTO maintains the established benefits of factor analysis for multi-modal data, but enables performing spatio-temporally informed dimensionality reduction, interpolation and separation of smooth from non-smooth patterns of variation. Moreover, MEFISTO can integrate multiple related datasets by simultaneously identifying and aligning the underlying patterns of variation in a data-driven manner. We demonstrate MEFISTO through applications to an evolutionary atlas of mammalian organ development, where the model reveals conserved and evolutionary diverged developmental programs. In applications to a longitudinal microbiome study in infants, birth mode and diet were highlighted as major causes for heterogeneity in the temporally-resolved microbiome over the first years of life. Finally, we demonstrate that the proposed framework can also be applied to spatially resolved transcriptomics.

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