Results 21 to 30 of about 44,758 (308)

Seemingly unrelated time series model for forecasting the peak and short-term electricity demand: Evidence from the Kalman filtered Monte Carlo method

Heliyon, 2023
In this extant paper, a multivariate time series model using the seemingly unrelated times series equation (SUTSE) framework is proposed to forecast the peak and short-term electricity demand using time series data from February 2, 2014, to August 2 ...
Frank Kofi Owusu, Peter Amoako-Yirenkyi, Nana Kena Frempong, Akoto Yaw Omari-Sasu, Isaac Adjei Mensah, Henry Martin, Adu Sakyi   +6 more
doaj   +1 more source

Iran's Exchange Market in Five Episodes: Bayesian Estimation of Systematic Risk with MCMC Method [PDF]

Mathematics and Modeling in Finance
This paper estimates systematic risk in Iran’s foreign exchange market using a stochastic volatility model, analyzing five distinct episodes shaped by varying economic and political conditions. By tracing the evolution of volatility dynamics across these
Amir Mohsen Moradi, Mohsen Mehrara, Mahdieh Tahmasebi   +2 more
doaj   +1 more source

Estimating the Volume of the Solution Space of SMT(LIA) Constraints by a Flat Histogram Method

Algorithms, 2018
The satisfiability modulo theories (SMT) problem is to decide the satisfiability of a logical formula with respect to a given background theory. This work studies the counting version of SMT with respect to linear integer arithmetic (LIA), termed SMT(LIA)
Wei Gao, Hengyi Lv, Qiang Zhang, Dunbo Cai   +3 more
doaj   +1 more source

Sticky proposal densities for adaptive MCMC methods [PDF]

2016 IEEE Statistical Signal Processing Workshop (SSP), 2016
Monte Carlo (MC) methods are commonly used in Bayesian signal processing to address complex inference problems. The performance of any MC scheme depends on the similarity between the proposal (chosen by the user) and the target (which depends on the problem).
Luca Martino, Roberto Casarin, David Luengo   +2 more
openaire   +4 more sources

A fast algorithm for BayesB type of prediction of genome-wide estimates of genetic value

Genetics Selection Evolution, 2009
Genomic selection uses genome-wide dense SNP marker genotyping for the prediction of genetic values, and consists of two steps: (1) estimation of SNP effects, and (2) prediction of genetic value based on SNP genotypes and estimates of their effects.
Shepherd Ross, Solberg Trygve R, Meuwissen Theo HE, Woolliams John A   +3 more
doaj   +1 more source

MCMC and GLMs for estimating regression parameters: Evidence from non-life Egyptian insurance sector [PDF]

Journal of Humanities and Applied Social Sciences, 2019
Purpose – The purpose of this study is to estimate the linear regression parameters using two alternative techniques. First technique is to apply the generalized linear model (GLM) and the second technique is the Markov Chain Monte Carlo (MCMC) method ...
Mahmoud ELsayed, Amr Soliman
doaj   +1 more source

MCMC methods for integer least-squares problems [PDF]

2010 48th Annual Allerton Conference on Communication, Control, and Computing (Allerton), 2010
We consider the problem of finding the least-squares solution to a system of linear equations where the unknown vector has integer entries (or, more precisely, has entries belonging to a subset of the integers), yet where the coefficient matrix and given vector are comprised of real numbers.
Hassibi, Babak, Dimakis, Alexandros G., Papailiopoulos, Dimitris   +2 more
openaire   +2 more sources

EM algorithm for Bayesian estimation of genomic breeding values

BMC Genetics, 2010
Background In genomic selection, a model for prediction of genome-wide breeding value (GBV) is constructed by estimating a large number of SNP effects that are included in a model.
Iwata Hiroyoshi, Hayashi Takeshi
doaj   +1 more source

Hybrid Monte Carlo on Hilbert spaces [PDF]

, 2011
The Hybrid Monte Carlo (HMC) algorithm provides a framework for sampling from complex, high-dimensional target distributions. In contrast with standard Markov chain Monte Carlo (MCMC) algorithms, it generates nonlocal, nonsymmetric moves in the state ...
Beskos, A, Sanz-Serna, JM, Beskos, A., Pinski, F. J., Sanz-Serna, J.M., F.J. Pinski, J.M. Sanz-Serna, A. Beskos, Pinski, FJ, A.M. Stuart, Stuart, A.M., Stuart, AM, Sanz-Serna, J. M., Beskos, Alexandros, Stuart, A. M., Pinski, F.J.   +15 more
core   +1 more source

Genetic Operator-Based Particle Filter Combined with Markov Chain Monte Carlo for Data Assimilation in a Crop Growth Model

Agriculture, 2020
Particle filter has received increasing attention in data assimilation for estimating model states and parameters in cases of non-linear and non-Gaussian dynamic processes.
Alaa Jamal, Raphael Linker
doaj   +1 more source