Bayesian system identification of dynamical systems using large sets of training data: A MCMC solution

AbstractIn the last 20 years the applicability of Bayesian inference to the system identification of structurally dynamical systems has been helped considerably by the emergence of Markov chain Monte Carlo (MCMC) algorithms – stochastic simulation methods which alleviate the need to evaluate the intractable integrals which often arise during Bayesian analysis. In this paper specific attention is given to the situation where, with the aim of performing Bayesian system identification, one is presented with very large sets of training data. Building on previous work by the author, an MCMC algorithm is presented which, through combing Data Annealing with the concept of ‘highly informative training data’, can be used to analyse large sets of data in a computationally cheap manner. The new algorithm is called Smooth Data Annealing

Bayesian and Markov chain Monte Carlo methods for identifying nonlinear systems in the presence of uncertainty

In this paper, the authors outline the general principles behind an approach to Bayesian system identification and highlight the benefits of adopting a Bayesian framework when attempting to identify models of nonlinear dynamical systems in the presence of uncertainty. It is then described how, through a summary of some key algorithms, many of the potential difficulties associated with a Bayesian approach can be overcome through the use of Markov chain Monte Carlo (MCMC) methods. The paper concludes with a case study, where an MCMC algorithm is used to facilitate the Bayesian system identification of a nonlinear dynamical system from experimentally observed acceleration time histories

Estimating the parameters of dynamical systems from Big Data using Sequential Monte Carlo samplers

In this paper the authors present a method which facilitates computationally efficientparameter estimation of dynamical systems from a continuously growing set of measure-ment data. It is shown that the proposed method, which utilises Sequential Monte Carlosamplers, is guaranteed to be fully parallelisable (in contrast to Markov chain MonteCarlo methods) and can be applied to a wide variety of scenarios within structural dynam-ics. Its ability to allowconvergenceof one’s parameter estimates, as more data is analysed,sets it apart from other sequential methods (such as the particle filter)

Assessing the perceived quality of brachial artery Flow Mediated Dilation studies for inclusion in meta-analyses and systematic reviews: Description of data employed in the development of a scoring tool based on currently accepted guidelines

Brachial artery Flow Mediated Dilation (FMD) is widely used as a non-invasive measure of endothelial function. Adherence to expert consensus guidelines on FMD measurement has been found to be of vital importance to obtain reproducible data. This article lists the literature data which was considered in the development of a tool to aid in the objective judgement of the extent to which published studies adhered to expert guidelines for FMD measurement. Application of this tool in a systematic review of FMD studies © 2016 . (http://dx.doi.org/10.1016/j.atherosclerosis.2016.03.011) (Greyling et al., 2016 [1]) indicated that adherence to expert consensus guidelines is strongly correlated to the reproducibility of FMD data

Rhythmic expression of Nocturnin mRNA in multiple tissues of the mouse

BACKGROUND: Nocturnin was originally identified by differential display as a circadian clock regulated gene with high expression at night in photoreceptors of the African clawed frog, Xenopus laevis. Although encoding a novel protein, the nocturnin cDNA had strong sequence similarity with a C-terminal domain of the yeast transcription factor CCR4, and with mouse and human ESTs. Since its original identification others have cloned mouse and human homologues of nocturnin/CCR4, and we have cloned a full-length cDNA from mouse retina, along with partial cDNAs from human, cow and chicken. The goal of this study was to determine the temporal pattern of nocturnin mRNA expression in multiple tissues of the mouse. RESULTS: cDNA sequence analysis revealed a high degree of conservation among vertebrate nocturnin/CCR4 homologues along with a possible homologue in Drosophila. Northern analysis of mRNA in C3H/He and C57/Bl6 mice revealed that the mNoc gene is expressed in a broad range of tissues, with greatest abundance in liver, kidney and testis. mNoc is also expressed in multiple brain regions including suprachiasmatic nucleus and pineal gland. Furthermore, mNoc exhibits circadian rhythmicity of mRNA abundance with peak levels at the time of light offset in the retina, spleen, heart, kidney and liver. CONCLUSION: The widespread expression and rhythmicity of mNoc mRNA parallels the widespread expression of other circadian clock genes in mammalian tissues, and suggests that nocturnin plays an important role in clock function or as a circadian clock effector