A Field Laboratory for Evaluating In Situ

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A 0.76-pJ/Pulse 0.1-1 Gpps Microwatt IR-UWB CMOS Pulse Generator with Adaptive PSD Control Using A Limited Monocycle Precharge Technique

Document Version Author final version (often known as postprint) Link to publication from Aalborg University Citation for published version (APA)

The creative platform:a didactic approach for unlimited application of knowledge in interdisciplinary and intercultural groups

Document Version Early version, also known as pre-print Link to publication from Aalborg University Citation for published version (APA): Byrge, C., & Hansen, S. (2009). The creative platform: a didactic approach for unlimited application of knowledge in interdisciplinary and intercultural groups. European Journal of Engineering Education, 34(3), 235-250. 10.1080/03043790902902914 General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.? Users may download and print one copy of any publication from the public portal for the purpose of private study or research.? You may not further distribute the material or use it for any profit-making activity or commercial gain? You may freely distribute the URL identifying the publication in the public portal? Take down policy If you believe that this document breaches copyright please contact us at [email protected] providing details, and we will remove access to the work immediately and investigate your claim

In Proceedings of UAI-06 1 The AI&M Procedure for Learning from Incomplete Data

We investigate methods for parameter learning from incomplete data that is not missing at random. Likelihood-based methods then require the optimization of a profile likelihood that takes all possible missingness mechanisms into account. Optimizing this profile likelihood poses two main difficulties: multiple (local) maxima, and its very high-dimensional parameter space. In this paper a new method is presented for optimizing the profile likelihood that addresses the second difficulty: in the proposed AI&M (adjusting imputation and maximization) procedure the optimization is performed by operations in the space of data completions, rather than directly in the parameter space of the profile likelihood. We apply the AI&M method to learning parameters for Bayesian networks. The method is compared against conservative inference, which takes into account each possible data completion, and against EM. The results indicate that likelihoodbased inference is still feasible in the case of unknown missingness mechanisms, and that conservative inference is unnecessarily weak. On the other hand, our results also provide evidence that the EM algorithm is still quite effective when the data is not missing at random.

Reconstruction methods for inverse problems

Publisher's PDF, also known as Version of record Link to publication from Aalborg University Citation for published version (APA): Møller, S. (2002). Reconstruction methods for inverse problems. Aalborg: Department of Mathematica