In this paper we address the problem of reliably fitting parametric and semi-parametric models to spots in high density spot array images obtained in gene expression experiments. The goal is to measure the amount of label bound to an array element. A lot of spots can be modelled accurately by a Gaussian shape. In order to deal with highly overlapping spots we use robust M-estimators. When the p...

In this paper we address the problem of reliably fitting parametric and semi-parametric models to high density spot array images obtained in genetic expression experiments. The goal is to measure the amount of genetic material at specific spot locations. A lot of spots can be modelled accurately by a Gaussian shape. In order to deal with highly overlapping spots we use robust M-estimators. When...

We present a common framework for Bayesian emulation methodologies for multivariate output simulators, or computer models, that employ either parametric linear models or non-parametric Gaussian processes. Novel diagnostics suitable for multivariate covariance separable emulators are developed and techniques to improve the adequacy of an emulator are discussed and implemented. A variety of emula...

Current CAD systems encode shape semantics as so called parametric features on different levels of abstraction. Here we discuss an approach that combines parametric modeling with techniques from the field of knowledge representation and ontological reasoning. Parametric models refer to feature ontologies that model feature semantics on several levels of granularity. On higher levels the interre...

ne of the fundamental challenges in theater ballistic missile defense (TBMD) is ascertaining which element in the threat complex is the lethal object. To classify the lethal object and other objects in the complex, it is necessary to model how these objects will appear to TBMD sensors. This article describes a generic parametric approach to building classifier models. The process is illustrated...

Linear and time-invariant signal and system models are useful in compact characterization of acoustic transfer functions. In addition to compact representations of responses, such models are efficient in simulating acoustic systems for sound synthesis, artificial reverberation, etc. In this paper we propose parametric modeling techniques for room impulse responses (RIRs), insitu acoustic materi...

Smooth (C1-, C2-,...) curve reconstruction from noisy point samples is central to reverse engineering, medical imaging, etc. Unresolved issues in this problem are (1) high computational expenses, (2) presence of artifacts and outlier curls, (3) erratic behavior at self-intersections and sharp corners. Some of these issues are related to non-Nyquist (i.e. sparse) samples. Our work reconstructs c...

This review provides an introduction to the use of parametric modelling techniques for time series analysis, and in particular the application of autoregressive modelling to the analysis of physiological signals such as the human electroencephalogram. The concept of signal stationarity is considered and, in the light of this, both adaptive models, and non-adaptive models employing fixed or adap...

In Model-Based Diagnosis (MBD), the problem of computing a diagnosis in a strong-fault model (SFM) is computationally much harder than in a weak-fault model (WFM). For example, in propositional Horn models, computing the first minimal diagnosis in a weak-fault model (WFM) is in P but is NP-hard for strong-fault models. As a result, SFM problems of practical significance have not been studied in...

In parametric statistical inference, which we will be primarily concerned with in this course, the underlying distribution of the population is taken to be parametrized by a Euclidean parameter. In other words, there exists a subset Θ of k-dimensional Euclidean space such that the class of dsitributions P of the underlying population can be written as {Pθ : θ ∈ Θ}. One key assumption made at th...