Recent developments in the area of smart structures indicate that variable geometry / stiffness truss network is of fundamental importance in designing smart transformable structures and systems for space applications. This paper presents the conceptual design and dynamic modeling of a cooperative re-configurabel dual-arm robotic structure called Dual-Arm Cam-Lock Manipulator. The Manipulator i...

We present probabilistic multinomial Dirichlet classification model for multidimensional data and Gaussian process priors. Here, we have considered efficient computational method that can be used to obtain the approximate posteriors for latent variables and parameters needed to define the multiclass Gaussian process classification model. We first investigated the process of inducing a posterior...

We often build complex probabilistic models by composing simpler models—using one model to generate parameters or latent variables for another model. This allows us to express complex distributions over the observed data and to share statistical structure between different parts of a model. In this thesis, we present a space of matrix decomposition models defined by the composition of a small n...

We present an approach for evaluating disclosure risks for fully synthetic categorical data. The basic idea is to compute probability distributions of unknown confidential data values given the synthetic data and assumptions about intruder knowledge. We use a “worst-case” scenario of an intruder knowing all but one of the records in the confidential data. To create the synthetic data, we use a ...

We present an algorithm that infers the model structure of a mixture of factor analysers using an efficient and deterministic variational approximation to full Bayesian integration over model parameters. This procedure can automatically determine the optimal number of components and the local dimensionality of each component (Le. the number of factors in each factor analyser) . Alternatively it...

Figure: Averaged normalized importances for X1 from 100 simulated datasets (simulation process described below) for m=1,2,3,4 (left to right) with β1=(4,1,1,0.3) , corr(Xj,Xk)=ρ |j−k| with ρ=−0.9 to 0.9 in steps of 0.1 Grey line: true normalized LMG allocation; Black line: true normalized PMVD allocation : Variable importance (% MSE Reduction) from RF-CART; ×: Variable importance (% MSE Reducti...

Random Forests are a powerful classification technique, consisting of a collection of decision trees. One useful feature of Random Forests is the ability to determine the importance of each variable in predicting the outcome. This is done by permuting each variable and computing the change in prediction accuracy before and after the permutation. This variable importance calculation is similar t...

Selection of relevant genes for sample classification is a common task in most gene expression studies. As a powerful classification approach, random forest has been applied in this field, and it shows excellent performance compared with other classification methods. The measure of variable importance is the key of gene selection using random forest. However, the existing methods just consider ...