Inference in Gaussian state-space models with mixed effects for multiple epidemic dynamics. [PDF]
J Math Biol, 2022 Narci R, Delattre M, Larédo C, Vergu E.
europepmc +1 more source
Parameterisation and efficient MCMC estimation of non-Gaussian state space models\ud
, 2008 The impact of parameterisation on the simulation efficiency of Bayesian Markov chain Monte Carlo (MCMC) algorithms for\ud two non-Gaussian state space models is examined.
Forbes, Catherine +5 more
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Molecular Oncology, EarlyView.Drug resistance limits treatment success in a subset of lung cancers driven by ROS1 gene alterations. Using patient‐derived cells and computer simulations, we studied three key mutations and how they affect five targeted drugs. The mutations reduced drug effectiveness in different ways by altering protein structure and behavior.
Farhan Ul Haq +8 more
wiley +1 more source
Generalised Hyperbolic State-Space Models for Inference in Dynamic Systems
IEEE Open Journal of Signal ProcessingIn this work we study linear vector stochastic differential equation (SDE) models driven by the generalised hyperbolic (GH) Lévy process for inference in continuous-time non-Gaussian filtering problems. The GH family of stochastic processes offers
Yaman Kindap, Simon Godsill
doaj +1 more source
A Stochastic Approximation-Langevinized Ensemble Kalman Filter Algorithm for State Space Models with Unknown Parameters. [PDF]
J Comput Graph Stat, 2023 Dong T, Zhang P, Liang F.
europepmc +1 more source
Forecasting VARMA processes using VAR models and subspace-based state space models
VAR modelling is a frequent technique in econometrics for linear processes. VAR modelling offers some desirable features such as relatively simple procedures for model specification (order selection) and the possibility of obtaining quick non-iterative ...
del Hoyo, Juan +2 more
core
Block Sampler and Posterior Mode Estimation for A Nonlinear and Non-Gaussian State-Space Model with Correlated Errors [PDF]
This article introduces a new efficient simulation smoother and disturbance smoother for general state-space models where there exists a correlation between error terms of the measurement and state equations.
Toshiaki Watanabe, Yasuhiro Omori
core
Identification of nonlinear lateral flow immunoassay state-space models via particle filter approach
, 2012 This is the post-print of the Article. The official published version can be accessed from the link below - Copyright @ 2012 IEEEIn this paper, the particle filtering approach is used, together with the kernel smoothing method, to identify the state ...
Zidong Wang +9 more
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Molecular Oncology, EarlyView.BCL9 and BCL9L drive bladder cancer progression by enhancing β‐catenin signaling, promoting proliferation, migration, invasion, and organoid growth. Genetic depletion of BCL9(L) suppresses malignant phenotypes, while pharmacological disruption of the β‐catenin/BCL9(L) complex with ZW4864 inhibits canonical Wnt signaling and tumor‐associated cellular ...
Roland Kotolloshi +11 more
wiley +1 more source
Geodesic Dynamics for Constrained State-Space Models on Riemannian Manifolds
MathematicsWe present a geodesic dynamics framework for discrete-time state evolution on the unit sphere SN−1 that maintains exact unit-norm constraints through Riemannian exponential mapping.
Tianyu Wang +3 more
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