We discuss some issues in using mixtures of polynomials (MOPs) for inference in hybrid Bayesian networks. MOPs were proposed by Shenoy and West for mitigating the problem of integration in inference in hybrid Bayesian networks. In defining MOP for multi-dimensional functions, one requirement is that the pieces where the polynomials are defined are hypercubes. In this paper, we discuss relaxing ...

Despite significant improvements in lentivirus (LV) vector-based gene therapy there are still several safety risks using LV vectors including the potential formation of replication-competent LV particles. To address this shortcoming, we constructed a novel and safer gene transfer system using modified SIN-based LV gene transfer vectors. Central to our approach is a conditional deletion of the Ψ...

Imaging is a class of non-Bayesian methods for the revision of probability density functions originally proposed as a semantics for conditional logic. Two of these revision functions, standard imaging and general imaging, have successfully been applied to modelling information retrieval by Crestani and van Rijsbergen. Due to the problematic nature of a\direct" implementation of imaging revision...

Protein secondary structure (SS) prediction is important for studying protein structure and function. When only the sequence (profile) information is used as input feature, currently the best predictors can obtain ~80% Q3 accuracy, which has not been improved in the past decade. Here we present DeepCNF (Deep Convolutional Neural Fields) for protein SS prediction. DeepCNF is a Deep Learning exte...

This study proposes the RO-RA-RV structure for rule-based knowledge and integrates methods of conditional probability, vector matrices, and artificial intelligence to establish a conditional probability knowledge similarity algorithm and calculation system. This calculation system can quickly and accurately calculate rule-based knowledge similarity matrices and determine the relationship among ...

We present an algorithm for arc reversal in Bayesian networks with tree-structured conditional probability tables, and consider some of its advantages, especially for the simulation of dynamic probabilistic networks. In particular, the method allows one to produce CPTs for nodes involved in the reversal that exploit regularities in the conditional distributions. We argue that this approach alle...

We present an approach to truely higher order functional logic programming based on higher order narrowing Roughly speaking we model a higher order functional core language by higher order rewriting and extend it by logic variables For the integration of logic programs conditional rules are supported For solving goals in this framework we present a complete calcu lus for higher order conditiona...

Statistical mediation and moderation analysis are widespread throughout the behavioral sciences. Increasingly, these methods are being integrated in the form of the analysis of ―mediated moderation‖ or ―moderated mediation,‖ or what Hayes and Preacher (in press) call conditional process modeling. In this paper, I offer a primer on some of the important concepts and methods in mediation analysis...

A new iterative algorithm for the solution of minimization problems in infinitedimensional Hilbert spaces which involve sparsity constraints in form of `p-penalties is proposed. In contrast to the well-known algorithm considered by Daubechies, Defrise and De Mol, it uses hard instead of soft shrinkage. It is shown that the hard shrinkage algorithm is a special case of the generalized conditiona...