Results 271 to 280 of about 29,628 (317)

Spatiotemporal evolution and spatial benefit analysis of the coupling coordination of China's three medical systems. [PDF]

Front Public Health
Yue T, Zhu Z, Song Q, Li D, Deng J.
europepmc   +1 more source

ForestForTrees: An R package to infer forest structure from remote sensing data

Methods in Ecology and Evolution, EarlyView.
Abstract Estimating forest size‐abundance distributions is essential for understanding demographic processes and structural dynamics. Remote sensing provides broad coverage but often misses smaller trees hidden beneath the canopy, complicating inference of the full size distribution.
Adam J. Eichenwald, John M. Grady, Alyson East, Noah Charney, Sydne Record   +4 more
wiley   +1 more source

Efficient reporting delay calibration in spatial metapopulation models for reconstructing cross-regional epidemic dynamics. [PDF]

Biosaf Health
Li H, Teng Y, Zu Z, Chen J.
europepmc   +1 more source

Cost-effectiveness analysis based on two models of sacituzumab tirumotecan versus platinum-based chemotherapy as second-line treatment in EGFR-mutant non-small-cell lung cancer. [PDF]

Front Public Health
Jin L, Han L, Zeng J.
europepmc   +1 more source

Markov switching zero-inflated space-time multinomial models for comparing multiple infectious diseases. [PDF]

Biostatistics
Douwes-Schultz D, Schmidt AM, Freitas LP, Carvalho MS.   +3 more
europepmc   +1 more source

Para-Markov chains and related non-local equations. [PDF]

Fract Calc Appl Anal
Facciaroni L, Ricciuti C, Scalas E, Toaldo B.   +3 more
europepmc   +1 more source

Stochastically evolving graphs via edit semigroups. [PDF]

Proc Natl Acad Sci U S A
Chung F, Robertson SJ.
europepmc   +1 more source

Bounds on Fluctuations of First Passage Times for Counting Observables in Classical and Quantum Markov Processes. [PDF]

J Stat Phys
Bakewell-Smith G, Girotti F, Guţă M, P Garrahan J.   +3 more
europepmc   +1 more source

Geometric convergence of a general Markov chain

, 1996
openaire   +1 more source

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Generating General Levels using Markov Chains

2019 11th Computer Science and Electronic Engineering (CEEC), 2019
The use of machine learning techniques for content generation has recently emerged on the scene. Procedural Content Generation via Machine Learning is the generation of game content by models that have been trained on existing game content. The aim of this paper is to generate general video game levels using Markov chains.
Adeel Zafar, Ayesha Irfan, Muhammad Zeeshan Sabir   +2 more
openaire   +1 more source