On the optimization of initial conditions for a model parameter estimation

Matonoha, Ctirad; Papáček, Štěpán; Kindermann, Stefan

Displaying similar documents to “On the optimization of initial conditions for a model parameter estimation”

On two methods for the parameter estimation problem with spatio-temporal FRAP data

Papáček, Štěpán, Jablonský, Jiří, Matonoha, Ctirad

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FRAP (Fluorescence Recovery After Photobleaching) is a measurement technique for determination of the mobility of fluorescent molecules (presumably due to the diffusion process) within the living cells. While the experimental setup and protocol are usually fixed, the method used for the model parameter estimation, i.e. the data processing step, is not well established. In order to enhance the quantitative analysis of experimental (noisy) FRAP data, we firstly formulate the inverse problem...

On estimation of diffusion coefficient based on spatio-temporal FRAP images: An inverse ill-posed problem

Kaňa, Radek, Matonoha, Ctirad, Papáček, Štěpán, Soukup, Jindřich

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We present the method for determination of phycobilisomes diffusivity (diffusion coefficient $D$ ) on thylakoid membrane from fluorescence recovery after photobleaching (FRAP) experiments. This was usually done by analytical models consisting mainly of a simple curve fitting procedure. However, analytical models need some unrealistic conditions to be supposed. Our method, based on finite difference approximation of the process governed by the Fickian diffusion equation and on the minimization...

Estimation of the density of a determinantal process

Yannick Baraud (2013)

Confluentes Mathematici

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We consider the problem of estimating the density $Π$ of a determinantal process $N$ from the observation of $n$ independent copies of it. We use an aggregation procedure based on robust testing to build our estimator. We establish non-asymptotic risk bounds with respect to the Hellinger loss and deduce, when $n$ goes to infinity, uniform rates of convergence over classes of densities $Π$ of interest.

On risk reserve under distribution constraints

Mariusz Michta (2000)

Discussiones Mathematicae Probability and Statistics

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The purpose of this work is a study of the following insurance reserve model: $R (t) = η + \int_{0}^{t} p (s, R (s)) d s + \int_{0}^{t} σ (s, R (s)) d W_{s} - Z (t)$ , t ∈ [0,T], P(η ≥ c) ≥ 1-ϵ, ϵ ≥ 0. Under viability-type assumptions on a pair (p,σ) the estimation γ with the property: $i n f_{0 \leq t \leq T} P R (t) \geq c \geq γ$ is considered.

Distributed $H_{\infty}$ estimation for moving target under switching multi-agent network

Hu Chen, Qin Weiwei, He Bing, Liu Gang (2015)

Kybernetika

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In this paper, the distributed $H_{\infty}$ estimation problem is investigated for a moving target with local communication and switching topology. Based on the solution of the algebraic Riccati equation, a recursive algorithm is proposed using constant gain. The stability of the proposed algorithm is analysed by using the Lyapounov method, and a lower bound for estimation errors is obtained for the proposed common $H_{\infty}$ filter. Moreover, a bound for the $H_{\infty}$ parameter is obtained by means of the solution...

On orthogonal series estimation of bounded regression functions

Waldemar Popiński (2001)

Applicationes Mathematicae

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The problem of nonparametric estimation of a bounded regression function $f \in L ² ({[a, b]}^{d})$ , [a,b] ⊂ ℝ, d ≥ 1, using an orthonormal system of functions $e_{k}$ , k=1,2,..., is considered in the case when the observations follow the model $Y_{i} = f (X_{i}) + η_{i}$ , i=1,...,n, where $X_{i}$ and $η_{i}$ are i.i.d. copies of independent random variables X and η, respectively, the distribution of X has density ϱ, and η has mean zero and finite variance. The estimators are constructed by proper truncation of the function $f ̂ ₙ (x) = \sum_{k = 1}^{N (n)} c ̂_{k} e_{k} (x)$ , where the coefficients $c ̂ ₁, . . ., c ̂_{N (n)}$ ...

Routh-type $L_{2}$ model reduction revisited

Wiesław Krajewski, Umberto Viaro (2018)

Kybernetika

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A computationally simple method for generating reduced-order models that minimise the $L_{2}$ norm of the approximation error while preserving a number of second-order information indices as well as the steady-state value of the step response, is presented. The method exploits the energy-conservation property peculiar to the Routh reduction method and the interpolation property of the $L_{2}$ -optimal approximation. Two examples taken from the relevant literature show that the suggested techniques...

Estimating the conditional expectations for continuous time stationary processes

Gusztáv Morvai, Benjamin Weiss (2020)

Kybernetika

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One of the basic estimation problems for continuous time stationary processes $X_{t}$ , is that of estimating $E {X_{t + β} | X_{s} : s \in [0, t]}$ based on the observation of the single block ${X_{s} : s \in [0, t]}$ when the actual distribution of the process is not known. We will give fairly optimal universal estimates of this type that correspond to the optimal results in the case of discrete time processes.