Non-parametric Bayesian modellingContinuous density HMM mixture modelDirichlet process mixture modelTridiagonal covarianceIn this paper, we present a non-parametric continuous density Hidden Markov mixture model (CDHMMix model) with unknown number of mixtures for blind segmentation or clustering of sequences...
We propose a framework for Bayesian non-parametric estimation of the rate at which new infections occur assuming that the epidemic is partially observed. The developed methodology relies on modelling the rate at which new infections occur as a function which only depends on time. Two different ...
Prior specification for non-parametric Bayesian inference involves the difficult task of quantifying prior knowledge about a parameter of high, often infinite, dimension. A statistician is unlikely to have informed opinions about all aspects of such a parameter but will have real information about ...
Some approaches such as non-parametric Bayesian inference methods provide less mechanistic information but they may nevertheless provide realistic representa- tions of complex regulatory interactions between genes, which a simple ODE system might not be able to cap- ture [16], especially when accurate...
To obtain the previous information of the diseases, the authors use non-parametric Bayesian hazard regression and posterior predictive inference to conduct a risk assessment. In this article, the “risk” refers to the probability of an adverse health-related event at a specific time, given the ...
An ad-hoc approach to the classical inference is presented for each ofthe two situations and compared with the Bayesian approach discussed. To illustrate the theory developed, data from clinical trials of severe head trauma patients at the Medical College of Virginia Head Injury Center from 1984 ...
Multi-modal distance metric learning: A bayesian non-parametric approach. In European Conference on Computer Vision, pages 63-77. Springer, 2014.Behnam Babagholami-Mohamadabadi, Seyed Mahdi Roostaiyan, Ali Zarghami, and Mahdieh Soleymani Baghshah, "Multi-modal distance metric learning: Abayesian ...
Bayesian and Non-Bayesian Inference for Survival Data Using Generalised Exponential DistributionStatistics and ProbabilityA two-parameter lifetime distribution was introduced by Kundu and Gupta known as generalised exponential distribution. This distribution has been touted to be an alternative to the well-...
An investigation of Bayesian inference approach to model validation with non-normal data. Journal of Statistical Computation and Simulation. http://dx.doi.org/10.1080/ 00949655.2012.672572.Jiang, X., Yuan, Y., Mahadevan, S., and Liu, X. (2013b). "An investigation of Bayesian inference ...
While our objective is improved prediction for complex relationships involving compositional response data, a disadvantage of non-parametric regression strategies, in general, is that the quantification and usual statistical inference of the effects of the independent variables is not straightforward. However...