Results 71 to 80 of about 24,420 (193)
On the Integral of the Fractional Brownian Motion and Some Pseudo-Fractional Gaussian Processes
Mathematics, 2019 We investigate the main statistical parameters of the integral over time of the fractional Brownian motion and of a kind of pseudo-fractional Gaussian process, obtained as a classical Gauss−Markov process from Doob representation by replacing ...
Mario Abundo, Enrica Pirozzi
doaj +1 more source
Intervention in Ornstein-Uhlenbeck SDEs [PDF]
, 2013 We introduce a notion of intervention for stochastic differential equations and a corresponding causal interpretation. For the case of the Ornstein-Uhlenbeck SDE, we show that the SDE resulting from a simple type of intervention again is an Ornstein ...
Sokol, Alexander
core
A Comparative Review of Specification Tests for Diffusion Models
International Statistical Review, EarlyView.Summary Diffusion models play an essential role in modelling continuous‐time stochastic processes in the financial field. Therefore, several proposals have been developed in the last decades to test the specification of stochastic differential equations.
A. López‐Pérez +3 more
wiley +1 more source
Journal of Integrative Plant Biology, EarlyView.Some African Cyphostemma species evolved much larger genomes as they adapted to dry, rocky habitats. These expansions are linked to succulent traits and specialization on nutrient‐rich limestone outcrops. The findings show how climate‐driven aridification shaped plant evolution and highlight broader genome‐environment patterns across flowering plants ...
Rindra M. Ranaivoson +18 more
wiley +1 more source
Maximum Likelihood Estimators for a Supercritical Branching Diffusion Process
International Journal of Mathematics and Mathematical Sciences, 2012 The log-likelihood of a nonhomogeneous Branching Diffusion Process under several conditions assuring existence and uniqueness of the diffusion part and nonexplosion of the branching process.
Pablo Olivares, Janko Hernandez
doaj +1 more source
Financial Time Series Uncertainty: A Review of Probabilistic AI Applications
Journal of Economic Surveys, EarlyView.ABSTRACT Probabilistic machine learning models offer a distinct advantage over traditional deterministic approaches by quantifying both epistemic uncertainty (stemming from limited data or model knowledge) and aleatoric uncertainty (due to inherent randomness in the data), along with full distributional forecasts.
Sivert Eggen +4 more
wiley +1 more source
Minimum Information Variability in Linear Langevin Systems via Model Predictive Control
EntropyControlling the time evolution of a probability distribution that describes the dynamics of a given complex system is a challenging problem. Achieving success in this endeavour will benefit multiple practical scenarios, e.g., controlling mesoscopic ...
Adrian-Josue Guel-Cortez +2 more
doaj +1 more source
Stochastic Theories and Deterministic Differential Equations
Advances in Mathematical Physics, 2010 We discuss the concept of “hydrodynamic” stochastic theory, which is not based on the traditional Markovian concept. A Wigner function developed for friction is used for the study of operators in quantum physics, and for the construction of a quantum ...
John F. Moxnes, Kjell Hausken
doaj +1 more source
An Exchange Rate Target Zone Model with a Terminal Condition and Mean-Reverting Fundamentals [PDF]
, 2015 This paper proposes a target zones exchange rate model with a terminal condition of entering a currency zone. It is assumed that the exchange rate is a function of the fundamental and time.
Ajevskis, Viktors
core +1 more source
A set-indexed Ornstein-Uhlenbeck process
, 2012 The purpose of this article is a set-indexed extension of the well-known Ornstein-Uhlenbeck process. The first part is devoted to a stationary definition of the random field and ends up with the proof of a complete characterization by its $L^2 ...
Balança, Paul, Herbin, Erick
core +3 more sources