Genotypic resistance analysis on viral DNA and plasma was performed in 56 therapy naive patients with recent and chronic infection to assess the prevalence of mutations associated with drug resistance and compare cell-free and cell-associated strains. Direct sequencing of DNA provirus disclosed key mutations to RT inhibitors more frequently than in plasma RNA. In addition, major mutations assoc...

This paper proves that for no prior probability distribution does the bootstrap (BS) distribution equal the predictive distribution, for all Bernoulli trials of some xed size. It then proves that for no prior will the BS give the same rst two moments as the predictive distribution for all size trials. It ends with an investigation of whether the BS can get the variance correct.

The prediction distribution of generalized geometric series distribution (GGSD) and of its truncated and size-biased forms is derived and studied under the non-informative and beta prior distributions. The prediction distributions for all the models are beta distribution, but the parameters of the prediction distributions depend on the choice of the prior distribution as well as the model under...

Based on geometric invariance properties, we derive an explicit prior distribution for the parameters of multivariate linear regression problems in the absence of further prior information. The problem is formulated as a rotationally-invariant distribution of L-dimensional hyperplanes inN dimensions, and the associated system of partial differential equations is solved. The derived prior distri...

One of the basic assumptions in Bayesian inspection models is that we have some prior knowledge about the number of defects in a certain product or software system. The prior knowledge can be often described as a probability distribution (e.g., Poisson distribution). In the paper, we propose three conditions that should be put forth as desirable properties for a prior probability distribution o...

When facing small numbers of observations or rare events, political scientists often encounter separation, in which explanatory variables perfectly predict binary events or non-events. In this situation, maximum likelihood provides implausible estimates and the researcher might want incorporate some form of prior information into the model. The most sophisticated research uses Jeffreys’ invaria...

Maximum Likelihood (ML) estimation is extensively used for estimating emission densities from clumped and incomplete nzeasurement data in Positron Emission Tomography (PEU modality. Reconstruction produced by ML-algorithm has been found noisy because it does not make use of available prior knowledge. Bayesian estimation provides such a platform for the inclusion of prior knowledge in the recons...

We propose the use of Bayesian surprise to detect arbitrary, salient acoustic events. We use Gaussian or Gamma distributions to model the spectrogram distribution and use the Kullback-Leibler divergence of the posterior and prior distribution to calculate how “unexpected” and thus surprising newly observed audio samples are. This way, we efficiently detect arbitrary surprising/salient acoustic ...

There has been a recent growth in the use of Bayesian methods in medical research. The main reasons for this are the development of computer intensive simulation based methods such as Markov chain Monte Carlo (MCMC), increases in computing power and the introduction of powerful software such as WinBUGS. This has enabled increasingly complex models to be fitted. The ability to fit these complex ...

The paper deals with a specification of the prior distribution of the initial state for Kalman filter. The subjective prior knowledge, used in state estimation, can be highly uncertain. In practice, incorporation of prior knowledge contributes to a good start of the filter. The present paper proposes a methodology for selection of the initial state distribution, which enables eliciting of prior...