3 citations · 5 across the 12 of their papers we have counts for
14 papers
Parameter Estimation for the Mixed Fractional Merton Jump Diffusion Model with EM Algorithm
Chidiogo Joy Agboeke, Hamidreza Maleki Almani, Dario Gasbarra +2
This paper proposes an Expectation--Maximization algorithm with Metropolis--Hastings sampling for parameter estimation in a Mixed Fractional Merton Jump Diffusion model. The model…
Is control of type I error rate needed in Bayesian clinical trial designs?
Elja Arjas, Dario Gasbarra
Practical employment of Bayesian trial designs is still rare. Even if accepted in principle, the regulators have commonly required that such designs be calibrated according to an u…
Polynomial Stein operators: a noncommutative algebra perspective
Ehsan Azmoodeh, Dario Gasbarra, Robert E. Gaunt
In this paper, we make a novel connection between Stein's method and noncommutative algebra by viewing polynomial Stein operators (Stein operators with polynomial coefficients) as…
An asymptotic approach to proving sufficiency of Stein characterisations
Ehsan Azmoodeh, Dario Gasbarra, Robert E. Gaunt
In extending Stein's method to new target distributions, the first step is to find a Stein operator that suitably characterises the target distribution. In this paper, we introduce…
Adaptive treatment allocation and selection in multi-arm clinical trials: a Bayesian perspective
Elja Arjas, Dario Gasbarra
Clinical trials are an instrument for making informed decisions based on evidence from well-designed experiments. Here we consider adaptive designs mainly from the perspective of m…
On algebraic Stein operators for Gaussian polynomials
Ehsan Azmoodeh, Dario Gasbarra, Robert E. Gaunt
The first essential ingredient to build up Stein's method for a continuous target distribution is to identify a so-called \textit{Stein operator}, namely a linear differential oper…
On a new Sheffer class of polynomials related to normal product distribution
Ehsan Azmoodeh, Dario Gasbarra
Consider a generic random element in the second Wiener chaos with a finite number of non-zero coefficients in the spectral represent…
Estimation of Viterbi path in Bayesian hidden Markov models
Jüri Lember, Dario Gasbarra, Alexey Koloydenko +1
The article studies different methods for estimating the Viterbi path in the Bayesian framework. The Viterbi path is an estimate of the underlying state path in hidden Markov model…
New moments criteria for convergence towards normal product/tetilla laws
Ehsan Azmoodeh, Dario Gasbarra
In the framework of classical probability, we consider the normal product distribution where are two independent standard normal random va…
Eigenvalues of random matrices with isotropic Gaussian noise and the design of Diffusion Tensor Imaging experiments
Dario Gasbarra, Sinisa Pajevic, Peter J. Basser
Tensor-valued and matrix-valued measurements of different physical properties are increasingly available in material sciences and medical imaging applications. The eigenvalues and…
Convergence rate for the hedging error of a path-dependent example
Dario Gasbarra, Anni Laitinen
We consider a Brownian functional with and a singular deterministic . We deduce the -convergence rate for the approximati…
Fast Estimation of Diffusion Tensors under Rician noise by the EM algorithm
Jia Liu, Dario Gasbarra, Juha Railavo
This paper presents a fast computational method, the Expectation Maximization algorithm, for Maximum Likelihood (ML) estimation in diffusion tensor imaging under the Rice noise mod…
Data augmentation in Rician noise model and Bayesian Diffusion Tensor Imaging
Dario Gasbarra, Jia Liu, Juha Railavo
Mapping white matter tracts is an essential step towards understanding brain function. Diffusion Magnetic Resonance Imaging (dMRI) is the only noninvasive technique which can detec…
Initial Enlargement in a Markov chain market model
Dario Gasbarra, José Igor Morlanes, Esko Valkeila
Enlargement of filtrations is a classical topic in the general theory of stochastic processes. This theory has been applied to stochastic finance in order to analyze models with in…