Inferential framework for nonstationary dynamics. I. Theory
نویسندگان
چکیده
منابع مشابه
Inferential framework for nonstationary dynamics. I. Theory.
A general Bayesian framework is introduced for the inference of time-varying parameters in nonstationary, nonlinear, stochastic dynamical systems. Its convergence is discussed. The performance of the method is analyzed in the context of detecting signaling in a system of neurons modeled as FitzHugh-Nagumo (FHN) oscillators. It is assumed that only fast action potentials for each oscillator mixe...
متن کاملInferential framework for non-stationary dynamics: theory and applications
An extended Bayesian inference framework is presented, aiming to infer time-varying parameters in non-stationary nonlinear stochastic dynamical systems. The convergence of the method is discussed. The performance of the technique is studied using, as an example, signal reconstruction for a system of neurons modeled by FitzHugh–Nagumo oscillators: it is applied to reconstruction of the model par...
متن کاملInferential framework for nonstationary dynamics. II. Application to a model of physiological signaling.
The problem of how to reconstruct the parameters of a stochastic nonlinear dynamical system when they are time-varying is considered in the context of online decoding of physiological information from neuron signaling activity. To model the spiking of neurons, a set of FitzHugh-Nagumo (FHN) oscillators is used. It is assumed that only a fast dynamical variable can be detected for each neuron, a...
متن کاملInferential framework for nonstationary dynamics Part II: Application to a model of physiological signaling
The problem of how to reconstruct the parameters of a stochastic nonlinear dynamical system when these are time-varying is considered in the context of online decoding of physiological information from neuron signaling activity. To model the spiking of neurons, a set of FitzHugh-Nagumo (FHN) oscillators is used. It is assumed that only a fast dynamical variable can be detected for each neuron, ...
متن کاملGeneral Theory of Inferential Models I. Conditional Inference
As applied problems have grown more complex, statisticians have been gradually led to reconsider the foundations of statistical inference. The recently proposed inferential model (IM) framework of Martin, Zhang and Liu (2010) achieves an interesting compromise between the Bayesian and frequentist ideals. Indeed, inference is based on posterior probability-like quantities, but there are no prior...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Physical Review E
سال: 2008
ISSN: 1539-3755,1550-2376
DOI: 10.1103/physreve.77.061105