Parameter uncertainties calculated using asymptotic approximation around the maximum likelihood estimates were often larger than uncertainties based on Markov chain Monte Carlo sampling for the same parameters. Diagnostics applied to MCMC samples in the best model of each configuration that obtained an ...
Parameter estimation using Eq. (35) can be carried out with standard non-linear optimisation algorithms such as quasi–Newton or conjugate gradients. In the original paper of [14], the penalty parameter λs is inferred using AIC. For a given value of λs, Eq. (35) is optimised to ...
Currently sampling based methods, which are in general stochastic in nature, like Markov-Chain Monte Carlo(MCMC), are being commonly used for parameter estimation. The beauty of stochastic methods is that the computational cost grows, at the most, linearly in place of exponentially (as in grid ...
Particle MCMCParticle Gibbs samplerPiecewise deterministic processesSequential Monte CarloWe develop particle Gibbs samplers for static-parameter estimation in discretely observed piecewise deterministic process (PDPs). PDPs are stochastic processes that jump randomly at a countable number of stopping times but...
mini-RK2 plasmid, where the parameters are undetermined. Our findings not only confirm the utility of MCMC for accurate parameter estimation and dynamic modelling but also highlight the inherent limitations of this approach and the intricate challenges presented by conjugation systems that are not ...
One approach might be based on an MCMC sampling of the Bayesian posterior using Eqs. (4) and (5) as priors or some other appropriate choice. In fact, we have sampled the Bayesian posterior for our models, and find that the allowed range of many parameters is always dominated by the ...
AN MCMC ALGORITHM FOR PARAMETER ESTIMATION IN SIGNALS WITH HIDDEN INTERMITTENT INSTABILITY NAN CHEN ∗ , DIMITRIOS GIANNAKIS ∗ , RADU HERBEI † , AND ANDREW J. MAJDA ∗ Abstract. Prediction of extreme events is a highly important and challenging problemin science, engineering, finance, an...
1G). The posterior distribution of parameters was computed through MCMC sampling (Section 2.5). To validate this newly developed non-hierarchical Bayesian method, we evaluated the accuracy of parameter estimation using synthetic data with known true parameter values. Synthetic data of the two-...
Two common approaches are to either select parameters through MCMC sampling or to select parameters on a regular grid. For density estimation, we employ KDE, and AD is used to evaluate the model derivative for all examples presented in this paper. Algorithm 1 Pseudo-implementation of EPI in ...
Our approach demonstrates computational efficiency several orders of magnitude faster than the traditional Markov Chain Monte Carlo (MCMC) methods, while preserving the unbiasedness of parameter estimation. We show that machine learning technology has the potential to efficiently handle the vast parameter ...