Results 1 to 10 of about 144,645 (228)
Joint inference of exclusivity patterns and recurrent trajectories from tumor mutation trees
Nature Communications, 2023 Cancer progression is an evolutionary process shaped by both deterministic and stochastic forces. Multi-region and single-cell sequencing of tumors enable high-resolution reconstruction of the mutational history of each tumor and highlight the extensive ...
Xiang Ge Luo +2 more
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Inference and prediction for stochastic models of biological populations undergoing migration and proliferation. [PDF]
J R Soc InterfaceParameter inference is a critical step in the process of interpreting biological data using mathematical models. Inference provides a means of deriving quantitative, mechanistic insights from sparse, noisy data.
Simpson MJ, Plank MJ.
europepmc +2 more sources
Multifidelity multilevel Monte Carlo to accelerate approximate Bayesian parameter inference for partially observed stochastic processes [PDF]
Journal of Computational Physics, 2021 Models of stochastic processes are widely used in almost all fields of science. Theory validation, parameter estimation, and prediction all require model calibration and statistical inference using data.
D. Warne +3 more
semanticscholar +1 more source
Unsupervised Sampling Promoting for Stochastic Human Trajectory Prediction [PDF]
Computer Vision and Pattern Recognition, 2023 The indeterminate nature of human motion requires trajectory prediction systems to use a probabilistic model to formulate the multi-modality phenomenon and infer a finite set of future trajectories.
Guan-Hong Chen +3 more
semanticscholar +1 more source
Construct stochastic processes model to solve graphic and math problems
Applied and Computational Engineering, 2023 The use of probabilistic reasoning has been used to a variety of applications, such as image identification, computer diagnostics, stock price prediction, movie recommendation, and cyber intrusion detection.
S. Ding
semanticscholar +1 more source
Neural Diffusion Processes [PDF]
International Conference on Machine Learning, 2022 Neural network approaches for meta-learning distributions over functions have desirable properties such as increased flexibility and a reduced complexity of inference.
Vincent Dutordoir +3 more
semanticscholar +1 more source
Unsupervised relational inference using masked reconstruction
Applied Network Science, 2023 Problem setting Stochastic dynamical systems in which local interactions give rise to complex emerging phenomena are ubiquitous in nature and society. This work explores the problem of inferring the unknown interaction structure (represented as a graph ...
Gerrit Großmann +3 more
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Contrastive Conditional Neural Processes [PDF]
Computer Vision and Pattern Recognition, 2022 Conditional Neural Processes (CNPs) bridge neural net-works with probabilistic inference to approximate functions of Stochastic Processes under meta-learning settings.
Zesheng Ye, Lina Yao
semanticscholar +1 more source
Psychophysical identity and free energy [PDF]
, 2020 An approach to implementing variational Bayesian inference in biological systems is considered, under which the thermodynamic free energy of a system directly encodes its variational free energy.
Kiefer, Alex B.
core +2 more sources
A sparse expansion for deep Gaussian processes [PDF]
IISE Transactions, 2021 In this work, we use Deep Gaussian Processes (DGPs) as statistical surrogates for stochastic processes with complex distributions. Conventional inferential methods for DGP models can suffer from high computational complexity, as they require large-scale ...
Liang Ding, Rui Tuo, Shahin Shahrampour
semanticscholar +1 more source