We discuss how statistical inference techniques can be applied in the context of designing novel biological systems. Bayesian techniques have found widespread application and acceptance in the systems biology community, where they are used for both parameter estimation and model selection. Here we show that the same approaches can also be used in order to engineer synthetic biological systems b...

We introduce a new large margin approach to discriminative training of intractable discrete graphical models. Our approach builds on a convex quadratic programming relaxation of the MAP inference problem. The model parameters are trained directly within this restricted class of energy functions so as to optimize the predictions on the training data. We address the issue of how to parameterize t...

MAP inference for general energy functions remains a challenging problem. While most efforts are channeled towards improving the linear programming (LP) based relaxation, this work is motivated by the quadratic programming (QP) relaxation. We propose a novel MAP relaxation that penalizes the Kullback-Leibler divergence between the LP pairwise auxiliary variables, and QP equivalent terms given b...

Problems with truncated data occur in many areas, complicating estimation and inference. Regarding linear regression models, the ordinary least squares estimator is inconsistent and biased for these types of data and is therefore unsuitable for use. Alternative estimators, designed for the estimation of truncated regression models, have been developed. This paper presents the R package truncSP....

We examine a nonparametric least squares regression model where the regression function is endogenously selected from the family of continuous, monotonic increasing and globally concave functions that can be nondifferentiable. We show that this family of functions is perfectly represented by a subset of continuous, piece-wise linear functions whose intercept and slope coefficients are constrain...

The aim of this study is to employ the novel Adaptive Network-based Fuzzy Inference System to optimize the torque applied on the knee link of a rehabilitation robot. Given the special conditions of stroke or spinal cord injury patients, devices with minimum error are required for performing the rehabilitation exercises. After examining the anthropometric data tables of human body, parameters su...

The duality of sensory inference and motor control has been known since the 1960s recently recognized as commonality in computations required for posterior distributions Bayesian value functions optimal control. Meanwhile, an intriguing question about brain is why entire neocortex shares a canonical six-layer architecture while its anterior halves are engaged processing control, respectively. H...