Results 111 to 120 of about 79,186 (232)

An Inverse Laplace Transform on Lattice Spacing [PDF]

, 2013
Hirofumi Yamada
openalex   +1 more source

Numerical Inverse Laplace Transform Methods for Advection-Diffusion Problems [PDF]

, 2022
Kamran Kamran, Farman Ali Shah, Wael Hosny Fouad Aly, Hasan Aksoy, Fahad Alotaibi, Ibrahim Mahariq   +5 more
openalex   +1 more source

A Bayes factor framework for unified parameter estimation and hypothesis testing

British Journal of Mathematical and Statistical Psychology, EarlyView.
Abstract The Bayes factor, the data‐based updating factor of the prior to posterior odds of two hypotheses, is a natural measure of statistical evidence for one hypothesis over the other. We show how Bayes factors can also be used for parameter estimation.
Samuel Pawel
wiley   +1 more source

Alternative Method for Determining the Feynman Propagator of a Non-Relativistic Quantum Mechanical Problem

Symmetry, Integrability and Geometry: Methods and Applications, 2007
A direct procedure for determining the propagator associated with a quantum mechanical problem was given by the Path Integration Procedure of Feynman. The Green function, which is the Fourier Transform with respect to the time variable of the propagator,
Marcos Moshinsky, Emerson Sadurní, Adolfo del Campo   +2 more
doaj  

Variance Matrix Priors for Dirichlet Process Mixture Models With Gaussian Kernels

International Statistical Review, EarlyView.
Summary Bayesian mixture modelling is widely used for density estimation and clustering. The Dirichlet process mixture model (DPMM) is the most popular Bayesian non‐parametric mixture modelling approach. In this manuscript, we study the choice of prior for the variance or precision matrix when Gaussian kernels are adopted.
Wei Jing, Michail Papathomas, Silvia Liverani   +2 more
wiley   +1 more source

Markov Determinantal Point Process for Dynamic Random Sets

Journal of Time Series Analysis, EarlyView.
ABSTRACT The Law of Determinantal Point Process (LDPP) is a flexible parametric family of distributions over random sets defined on a finite state space, or equivalently over multivariate binary variables. The aim of this paper is to introduce Markov processes of random sets within the LDPP framework. We show that, when the pairwise distribution of two
Christian Gouriéroux, Yang Lu
wiley   +1 more source

Robust CDF‐Filtering of a Location Parameter

Journal of Time Series Analysis, EarlyView.
ABSTRACT This paper introduces a novel framework for designing robust filters associated with signal plus noise models having symmetric observation density. The filters are obtained by a recursion where the innovation term is a transform of the cumulative distribution function of the residuals.
Leopoldo Catania, Andrew C. Harvey, Alessandra Luati   +2 more
wiley   +1 more source

Disposition Analysis by Fast Inverse Laplace Transform (FILT)

, 1992
Kosaku Yamaoka
openalex   +2 more sources

Partial Observability of Implied Volatility Matrices: Identification and Covolatilities Filtering

Mathematical Finance, EarlyView.
ABSTRACT Whereas data on implied volatilities are available for a large number of assets, this is less frequently the case of implied covolatilities. We introduce a new approach based on static and dynamic Wishart models to solve this problem of missing data.
Christian Gouriéroux, Yang Lu
wiley   +1 more source

A meshless method based on the Laplace transform for multi-term time-space fractional diffusion equation

AIMS Mathematics
Multi-term fractional diffusion equations can be regarded as a generalisation of fractional diffusion equations. In this paper, we develop an efficient meshless method for solving the multi-term time-space fractional diffusion equation. First, we use the
Zihan Yue , Wei Jiang, Boying Wu, Biao Zhang   +3 more
doaj   +1 more source