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Bayesian Selective Transfer Learning for Patient-Specific Inference in Thyroid Radiotherapy
Murray, Sean Ernest ; Quinn, Anthony
This research report outlines a selective transfer approach for Bayesian estimation of patient-specific levels of radioiodine activity in the thyroid during the treatment of differentiated thyroid carcinoma. The work seeks to address some limitations of previous approaches [4] which involve generic, non-selective transfer of archival data. It is proposed that improvements in patient-specific inferences may be achieved via transferring external population knowledge selectively. This involves matching the patient to a similar sub-population based on available metadata, generating a Gaussian Mixture Model within the partitioned data, and optimally transferring a data predictive distribution from the sub-population to the specific patient. Additionally, a performance evaluation method is proposed and early-stage results presented.
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Subjective well-being and the individual material situation in Central Europe: A Bayesian network approach
Švorc, Jan ; Vomlel, Jiří
The objective of this paper is to explore the associations between the subjective well-being (SWB) and the subjective and objective measures of the individual material situation in the four post-communist countries of Central Europe (the Czech Republic, Hungary, Poland, and Slovakia). The material situation is measured by income, relative income compared to others, relative income compared to one’s own past, perceived economic strain, financial problems, material deprivation, and housing problems. Our analysis is based on empirical data from the third wave of European Quality of Life Study conducted in 2011. Bayesian networks as a graphical representation of the relations between SWB and the material situation have been constructed in five versions. The models have been assessed using the Bayesian Information Criterion (BIC) and SWB prediction accuracy, and compared\nwith Ordinal Logistic Regression (OLR). Expert knowledge, as well as three different algorithms (greedy, Gobnilp, and Tree-augmented Naive Bayes) were used for learning the network structures. Network parameters were learned using the EM algorithm. Parameters based on OLR were learned for a version of the expert model. The Gobnilp model, the Markov equivalent to the greedy model, is BIC optimal. The OLR predicts SWB slightly better than the other models. We conclude that the objective material conditions' influence on SWB is rather indirect, through the subjective situational assessment of various aspects related to the individual material conditions.
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Mixing of Predictors in Parameter Estimation
Podlesna, Yana ; Kárný, Miroslav
This bachelor thesis deals with the design of the method for solving the curse of dimensionality arising in the quantitative modeling of complex interconnected systems. The employed predictive models are based on a discrete Markov process. Prediction is based on estimating model parameters using Bayesian statistics. This work contains method for reducing the amount of data needed for prediction in systems with a large number of occurring states and actions. Instead of estimating a predictor dependent on all parameters, the method assumes the use of several predictors, which arise from estimating parametric models based on dependences on different regressors. The behavioral properties of the proposed method are illustrated by simulation experiments.
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Algorithmic Selection of Feasible Preferences
Siváková, Tereza ; Kárný, Miroslav
This bachelor’s thesis studies the optimal decision making for a discrete Markov decision process with a focus on preferences. By using a fully probabilistic design that introduces the so-called ideal behavior distribution, which has high probability values of preferred behaviors and small probability values of inappropriate behaviors, an optimal decision policy has been found. The thesis constructs an algorithm for selecting the optimal ideal behavior distribution and provides a more general solution than published ones. The thesis also opens a possibility to specify further preferences on selected actions. Properties of the resulting decision making are illustrated on simulated examples.
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Detection of weak signals in noise
Tichavský, Petr
In this report, a bound on amplitude of impuls signal detection in stationary background noise is computed.\nIt was evaluated for an experiment with acoustic emission from mechanically strained sample.
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DCTOOL-A5
Bakule, Lubomír ; Papík, Martin ; Rehák, Branislav
DCTOOL-A5 presents draft of a manuscript, which is intended to be submitted for publication. This report presents a new method for the decentralized event-triggered control design for large-scale uncertain systems. The results are formulated and proved in terms of linear matrix inequalities. Two design problems are solved: For interconnected systems without any quantization and for interconnected systems with local logarithmic quantizers. Results are illustrated by an example.
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DCTOOL-A4
Bakule, Lubomír ; Papík, Martin ; Rehák, Branislav
DCTOOL-A4 report presents draft of a manuscript, which is intended to be submitted for publication. The report provides a novel systematic approach to the analysis of asymptotic stability for output event-triggered uncertain centralized control systems. A class of nonlinear but nominally linear systems possessing unknown time-varying bounded uncertainties with known bounds is considered. Uncertainties are allowed in all system matrices. Original LMI-based suffi cient conditions are derived to guarantee asymptotic stability of closed-loop systems with both static output and observer-based feedback loop under even-triggered control. Both these output feedback strategies are extended to model-based uncertain control systems with\nquantized measurements. A logarithmic quantizer is considered. The Lyapunov-based approach and convex optimization serve as the main methods to derive the asymptotic LMI-based stability conditions. Bounds on the inter-event times to avoid the Zeno-effect are proved for all the cases considered. Finally, feasibility and effi ciency of the proposed strategies is demonstrated by providing numerical examples.
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