Number of found documents: 1571
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Bayesian Selective Transfer Learning for Patient-Specific Inference in Thyroid Radiotherapy
Murray, Sean Ernest; Quinn, Anthony
2020 - English
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. Keywords: Bayesian estimation; thyroid carcinoma; patient-specific inferences Fulltext is available at external website.
Bayesian Selective Transfer Learning for Patient-Specific Inference in Thyroid Radiotherapy

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 ...

Murray, Sean Ernest; Quinn, Anthony
Ústav teorie informace a automatizace, 2020

Bayesian transfer learning between autoregressive inference tasks
Barber, Alec; Quinn, Anthony
2020 - English
Bayesian transfer learning typically relies on a complete stochastic dependence speci cation between source and target learners which allows the opportunity for Bayesian conditioning. We advocate that any requirement for the design or assumption of a full model between target and sources is a restrictive form of transfer learning. Keywords: autoregression; transfer learning; Fully Probabilistic Design; FPD; food-commodities price prediction Fulltext is available at external website.
Bayesian transfer learning between autoregressive inference tasks

Bayesian transfer learning typically relies on a complete stochastic dependence speci cation between source and target learners which allows the opportunity for Bayesian conditioning. We advocate that ...

Barber, Alec; Quinn, Anthony
Ústav teorie informace a automatizace, 2020

Bivariate Geometric Distribution and Competing Risks: Statistical Analysis and Application
Volf, Petr
2020 - English
The contribution studies the statistical model for discrete time two-variate duration (time-to-event) data. The analysis is complicated by partial data observation caused either by the right-side censoring or by the presence of dependent competing events. The case is modeled and analyzed with the aid of a two-variate geometric distribution. The model identifiability is discussed and it is shown that the model is not identifiable without proper additional assumptions. The method of analysis is illustrated both on artificially generated\nexample and on real unemployment data. Keywords: bivariate geometric distribution; competing risks; unemployment data Fulltext is available at external website.
Bivariate Geometric Distribution and Competing Risks: Statistical Analysis and Application

The contribution studies the statistical model for discrete time two-variate duration (time-to-event) data. The analysis is complicated by partial data observation caused either by the right-side ...

Volf, Petr
Ústav teorie informace a automatizace, 2020

Use of the BCC and Range Directional DEA Models within an Efficiency Evaluation
Houda, Michal
2020 - English
The contribution deals with two data envelopment analysis (DEA) models, in particular the BCC model (radial DEA model with variable returns to scale), and the range directional model. The mathematical description of the models are provided and several properties reported. A numerical comparison of the two models on real industrial data is provided with discussion about possible drawbacks of simplifying modeling procedures. Keywords: Data Envelopment Analysis; BCC Model; Range Directional Model Fulltext is available at external website.
Use of the BCC and Range Directional DEA Models within an Efficiency Evaluation

The contribution deals with two data envelopment analysis (DEA) models, in particular the BCC model (radial DEA model with variable returns to scale), and the range directional model. The mathematical ...

Houda, Michal
Ústav teorie informace a automatizace, 2020

A Note on Stochastic Optimization Problems with Nonlinear Dependence on a Probability Measure
Kaňková, Vlasta
2020 - English
Nonlinear dependence on a probability measure begins to appear (last time) in a stochastic optimization rather often. Namely, the corresponding type of problems corresponds to many situations in applications. The nonlinear dependence can appear as in the objective functions so in a constraints set. We plan to consider the case of static (one-objective) problems in which nonlinear dependence appears in the objective function with a few types of constraints sets. In details we consider constraints sets “deterministic”, depending nonlinearly on the probability measure, constraints set determined by second order stochastic dominance and the sets given by mean-risk problems. The last case means that the constraints set corresponds to solutions those guarantee an acceptable value in both criteria. To introduce corresponding assertions we employ the stability results based on the Wasserstein metric and L1 norm. Moreover, we try to deal also with the case when all results have to be obtained (estimated) on the data base. Keywords: Stochastic optimization problem; Nonlinear dependence; Empirical estimates; Static problems Fulltext is available at external website.
A Note on Stochastic Optimization Problems with Nonlinear Dependence on a Probability Measure

Nonlinear dependence on a probability measure begins to appear (last time) in a stochastic optimization rather often. Namely, the corresponding type of problems corresponds to many situations in ...

Kaňková, Vlasta
Ústav teorie informace a automatizace, 2020

Kombinace prediktorů v odhadování parametrů
Podlesna, Yana; Kárný, Miroslav
2020 - Czech
Tato bakalářská práce se zabývá návrhem metody k řešení prokletí rozměrnosti vznikající v kvantitativním modelování složitých vzájemně propojených systémů. Jedná se o předpovídací modely, které jsou založené na diskrétním markovském rozhodovacím procesu. Předpovídání je založeno na odhadu parametrů modelu pomocí bayesovské statistiky. Tato práce obsahuje návod na zmenšení rozměrnosti dat, potřebných k předpovídání v systémech s velkým počtem stavů a akcí. Místo odhadu prediktoru závislého na všech parametrech metoda předpokládá užití několika prediktorů, které vznikají odhadováním parametrických modelů, předpokládajících závislost na různých regresorech. Vlastnosti chování navržené metody jsou ilustrovány simulačními experimenty. 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. Keywords: curse of dimensionality; Bayesian estimation; prediction; Markov decision process; decision making Fulltext is available at external website.
Kombinace prediktorů v odhadování parametrů

Tato bakalářská práce se zabývá návrhem metody k řešení prokletí rozměrnosti vznikající v kvantitativním modelování složitých vzájemně propojených systémů. Jedná se o předpovídací modely, které jsou ...

Podlesna, Yana; Kárný, Miroslav
Ústav teorie informace a automatizace, 2020

Potential Radioactive Hot Spots Induced by Radiation Accident Being Underway of Atypical Low Wind Meteorological Episodes
Pecha, Petr; Tichý, Ondřej; Pechová, E.
2020 - English
Hypothetical radioactivity release with potentially high variability of the source strength is examined. The interactions of the radioactive cloud with surface and atmospheric precipitation are studied and possible adverse consequences on the environment are estimated. The worst-case scenario is devised in two stages starting with a calm meteorological situation succeeded by wind. At the first stage, the discharges of radionuclides into the motionless ambient atmosphere are assumed. During several hours of this calm meteorological situation, a relatively significant level of radioactivity can be accumulated around the source. At the second stage, the calm is assumed to terminate and convective movement of the air immediately starts. The pack of accumulated radioactivity in the form of multiple Gaussian puffs is drifted by wind and pollution is disseminated over the terrain. The results demonstrate the significant transport of radioactivity even behind the protective zone of a nuclear facility (up to between 15 and 20 km). In the case of rain, the aerosols are heavily washed out and dangerous hot spots of the deposited radioactivity can surprisingly emerge even far from the original source of the pollution. Keywords: radioactivity; atmospheric dissemination; deposition hot-spots Fulltext is available at external website.
Potential Radioactive Hot Spots Induced by Radiation Accident Being Underway of Atypical Low Wind Meteorological Episodes

Hypothetical radioactivity release with potentially high variability of the source strength is examined. The interactions of the radioactive cloud with surface and atmospheric precipitation are ...

Pecha, Petr; Tichý, Ondřej; Pechová, E.
Ústav teorie informace a automatizace, 2020

DEnFi: Deep Ensemble Filter for Active Learning
Ulrych, Lukáš; Šmídl, Václav
2020 - English
Deep Ensembles proved to be a one of the most accurate representation of uncertainty for deep neural networks. Their accuracy is beneficial in the task of active learning where unknown samples are selected for labeling based on the uncertainty of their prediction. Underestimation of the predictive uncertainty leads to poor exploration of the method. The main issue of deep ensembles is their computational cost since multiple complex networks have to be computed in parallel. In this paper, we propose to address this issue by taking advantage of the recursive nature of active learning. Specifically, we propose several methods how to generate initial values of an ensemble based of the previous ensemble. We provide comparison of the proposed strategies with existing methods on benchmark problems from Bayesian optimization and active classification. Practical benefits of the approach is demonstrated on example of learning ID of an IoT device from structured data using deep-set based networks. Keywords: Deep Ensembles; uncertainty; neural networks Fulltext is available at external website.
DEnFi: Deep Ensemble Filter for Active Learning

Deep Ensembles proved to be a one of the most accurate representation of uncertainty for deep neural networks. Their accuracy is beneficial in the task of active learning where unknown samples are ...

Ulrych, Lukáš; Šmídl, Václav
Ústav teorie informace a automatizace, 2020

Risk-Sensitivity and Average Optimality in Markov and Semi-Markov Reward Processes
Sladký, Karel
2020 - English
This contribution is devoted to risk-sensitivity in long-run average optimality of Markov and semi-Markov reward processes. Since the traditional average optimality criteria cannot reflect the variability-risk features of the problem, we are interested in more sophisticated approaches where the stream of rewards generated by the Markov chain that is evaluated by an exponential utility function with a given risk sensitivity coefficient. Recall that for the risk sensitivity coefficient equal to zero (i.e. the so called risk-neutral case) we arrive at traditional optimality criteria, if the risk sensitivity coefficient is close to zero the Taylor expansion enables to evaluate variability of the generated total reward. Observe that the first moment of the total reward corresponds to expectation of total reward and the second central moment to the reward variance. In this note we present necessary and sufficient risk-sensitivity and risk-neutral optimality conditions for long run risk-sensitive average optimality criterion of unichain Markov and semi-Markov reward processes. Keywords: Markov and semi-Markov reward processes; exponential utility function; risk sensitivity Fulltext is available at external website.
Risk-Sensitivity and Average Optimality in Markov and Semi-Markov Reward Processes

This contribution is devoted to risk-sensitivity in long-run average optimality of Markov and semi-Markov reward processes. Since the traditional average optimality criteria cannot reflect the ...

Sladký, Karel
Ústav teorie informace a automatizace, 2020

Macroeconomic Responses of Emerging Market Economies to Oil Price Shocks: Analysis by Region and Resource Profile
Togonidze, S.; Kočenda, Evžen
2020 - English
This study employs a vector autoregressive (VAR) model to analyse how oil price shocks affect macroeconomic fundamentals in emerging economies. Findings from existing literature remain inconclusive how macroeconomic variables fare towards shocks, especially in emerging economies. The objective of our study is to uncover if analysis by region (Latin America and the Caribbean, East Asia and the Pacific, Europe, and Central Asia) and resource intensity of economies (oil exporters, oil importers, minerals exporters, and less resource intensive). Our unique approach forms part of our contribution to the literature. We find that Latin America and the Caribbean are least affected by oil price shocks, while in East Asia and the Pacific the response of inflation and interest rate to oil price shocks is positive, and output growth is negative. Our analysis by resource endowment fails to show oil price shocks’ ability to explain huge variations in macroeconomic variables in oil importing economies. Further sensitivity analysis using US interest rates as an alternative source of external shocks to emerging economies establishes a significant response of interest rate responses to US interest rate in Europe and Central Asia, and in inflation in Latin America and the Caribbean. We also find that regardless of resource endowment, the response of output growth and capital to a positive US interest rate shock is negative and significant in EMs. Our results are persuasive that resource intensity and regional factors impact the responsiveness of emerging economies to oil price shocks, thus laying a basis for policy debate.\n Keywords: Emerging market economies; Oil price shocks; Output growth; Panel VAR Fulltext is available at external website.
Macroeconomic Responses of Emerging Market Economies to Oil Price Shocks: Analysis by Region and Resource Profile

This study employs a vector autoregressive (VAR) model to analyse how oil price shocks affect macroeconomic fundamentals in emerging economies. Findings from existing literature remain inconclusive ...

Togonidze, S.; Kočenda, Evžen
Ústav teorie informace a automatizace, 2020

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