Results 91 to 100 of about 31,731,507 (308)
Risks, 2017 This paper gives a detailed overview of the current state of research in relation to the use of state space models and the Kalman-filter in the field of stochastic claims reserving.
Nataliya Chukhrova, Arne Johannssen
doaj +1 more source
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
core
Subspace system identification
Iranian Journal of Electrical and Electronic Engineering, 2005 We give a general overview of the state-of-the-art in subspace system identification methods. We have restricted ourselves to the most important ideas and developments since the methods appeared in the late eighties.
J. Poshtan, H. Mojallali
doaj
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
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EnergiesThis paper presents a new method that should make it possible to determine the sensitivity of uncontrolled electrical power grids with only a modest amount of effort.
Tobias Blenk, Christian Weindl
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IEEE Access, 2019 The autonomic nervous system (ANS) stimulates various sweat glands for maintaining body temperature as well as in response to various psychological events. Variations in skin conductance (SC) measurements due to salty sweat secretion can be used to infer
MD. Rafiul Amin, Rose T. Faghih
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Molecular Oncology, EarlyView.In the present work, we have identified a transcriptional signature based on the differential expression of six genes (BCL2&MAST4, HSH2D&LAT2, METRN&PITPNM2) that would facilitate the early detection of T‐cell acute lymphoblastic leukemia (T‐ALL) patients prone to a poor treatment response and could be implemented at diagnosis, along with other risk ...
Antonio Lahera +11 more
wiley +1 more source