Results 11 to 20 of about 38,389 (265)

Bayesian parameter inference by Markov chain Monte Carlo with hybrid fitness measures: theory and test in apoptosis signal transduction network. [PDF]

PLoS ONE, 2013
When model parameters in systems biology are not available from experiments, they need to be inferred so that the resulting simulation reproduces the experimentally known phenomena. For the purpose, Bayesian statistics with Markov chain Monte Carlo (MCMC)
Yohei Murakami, Shoji Takada
doaj   +1 more source

A splitting method to reduce MCMC variance

CoRR, 2020
We explore whether splitting and killing methods can improve the accuracy of Markov chain Monte Carlo (MCMC) estimates of rare event probabilities, and we make three contributions. First, we prove that "weighted ensemble" is the only splitting and killing method that provides asymptotically consistent estimates when combined with MCMC. Second, we prove
Robert J. Webber, David Aristoff, Gideon Simpson   +2 more
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Laplace approximation for conditional autoregressive models for spatial data of diseases

MethodsX, 2022
Conditional autoregressive (CAR) distributions are used to account for spatial autocorrelation in small areal or lattice data to assess the spatial risks of diseases.
Guiming Wang
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Alternatives To The MCMC Method [PDF]

AIP Conference Proceedings, 2004
The Markov Chain Monte Carlo method (MCMC) is often used to generate independent (pseudo) random numbers from a distribution with a density that is known only up to a normalising constant. With the MCMC method it is not necessary to compute the normalising constant (see e.g. Tierney, 1994; Besag, 2000).
openaire   +2 more sources

Bayesian inference on reliability parameter with non-identical-component strengths for Rayleigh distribution [PDF]

Journal of Mahani Mathematical Research
In this paper, we delve into Bayesian inference related to multi-component stress-strength parameters, focusing on non-identical component strengths within a two-parameter Rayleigh distribution under the progressive first failure censoring scheme.
Akram Kohansal
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
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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
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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
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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
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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
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