Results 91 to 100 of about 31,735,543 (308)
State space c-reductions for concurrent systems in rewriting logic [PDF]
, 2011 We present c-reductions, a state space reduction technique. The rough idea is to exploit some equivalence relation on states (possibly capturing system regularities) that preserves behavioral properties, and explore the induced quotient system.
Lluch-Lafuente, Alberto +8 more
core +1 more source
FEBS Letters, EarlyView.Transient oligomers formed by intrinsically disordered proteins may be ‘invisible’ to direct detection yet remain accessible to solution NMR through equilibrium‐exchange measurements and pressure‐jump experiments. Complementary methods report on mass, stoichiometry, selected distance distributions, morphology, and internal packing.
Martin D. Gelenter, Ad Bax
wiley +1 more source
Fast Filtering and Smoothing for Multivariate State Space Models [PDF]
This paper gives a new approach to diffuse filtering and smoothing for multivariate state space models. The standard approach treats the observations as vectors while our approach treats each element of the observational vector individually.
Koopman, S.J.M., Durbin, J.
core
Bootstrap Approximation to Prediction MSE for State-Space Models with Estimated Parameters
, 2004 We propose a simple but general bootstrap method for estimating the Prediction Mean Square Error (PMSE) of the state vector predictors when the unknown model parameters are estimated from the observed series.
Pfeffermann, Danny, Tiller, Richard
core +2 more sources
Membrane composition and thermodynamic identity as boundaries of life for synthetic cell research
FEBS Letters, EarlyView.What makes a cell a cell? The boundary of a living cell is not just a wall. Read as a Markov blanket, the membrane separates internal from external states, generating identity and non‐equilibrium order. Can this identity be rebuilt from scratch in a synthetic cell?
Caterina Presutti, Bert Poolman
wiley +1 more source
Forecasting Australian Macroeconomic variables, evaluating innovations state space approaches [PDF]
Innovations state space time series models that encapsulate the exponential smoothing methodology have been shown to be an accurate forecasting tool. These models for the first time are applied to Australian macroeconomic data.
de Silva, Ashton J
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IEEE AccessAccurate target tracking is essential for the effectiveness of air surveillance, air-traffic monitoring, and defense-support systems. This paper presents a comparative study of several dynamic motion models applied to Kalman-based filters for aerial ...
Arthur Medeiros Macedo +1 more
doaj +1 more source
Tumour–host interactions in Drosophila: mechanisms in the tumour micro‐ and macroenvironment
Molecular Oncology, EarlyView.This review examines how tumour–host crosstalk takes place at multiple levels of biological organisation, from local cell competition and immune crosstalk to organism‐wide metabolic and physiological collapse. Here, we integrate findings from Drosophila melanogaster studies that reveal conserved mechanisms through which tumours hijack host systems to ...
José Teles‐Reis, Tor Erik Rusten
wiley +1 more source
Inference for Adaptive Time Series Models: Stochastic Volatility and Conditionally Gaussian State Space Form [PDF]
In this paper we replace the Gaussian errors in the standard Gaussian, linear state space model with stochastic volatility processes. This is called a GSSF-SV model.
Neil Shephard, Charles S. Bos
core
Sequential Monte Carlo methods for parameter estimation in nonlinear state-space models
, 2012 Stochastic nonlinear state-space models (SSMs) are prototypical mathematical models in geoscience. Estimating unknown parameters in nonlinear SSMs is an important issue for environmental modeling.
Zhang, Hui, Gao, Meng
core +1 more source