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ECB monetary policy and commodity prices
Aliyev, S.; Kočenda, Evžen
2020 - English
We analyze the impact of the ECB monetary policies on global aggregate and sectoral commodity prices using monthly data from January 2001 till August 2019. We employ a SVAR model and assess separately period of conventional monetary policy before global financial crisis (GFC) and unconventional monetary policy during post-crisis period. Our key results indicate that contractionary monetary policy shocks have positive effects on the aggregate and sectoral commodity prices during both conventional and unconvetional monetary policy periods. The effect is statistically significant for aggregate commodity prices during post-crisis period. In terms of sectoral impact, the effect is statistically significant for food prices in both periods and for fuel prices during post-crisis period; other commodities display positive but statistically insignificant responses. Further, we demonstrate that the impact of the ECB monetary policy on commodity prices increased remarkably after the GFC. Our results also suggest that the effect of the ECB monetary policy on commodity prices does not transmit directly through market demand and supply expectations channel, but rather through the exchange rate channel that influences the European market demand directly.
Keywords:
European Central Bank; commodity prices; monetary policy
Fulltext is available at external website.
ECB monetary policy and commodity prices
We analyze the impact of the ECB monetary policies on global aggregate and sectoral commodity prices using monthly data from January 2001 till August 2019. We employ a SVAR model and assess separately ...
Systems biology analysis of a drug metabolism (with slow-fast. . . )
Papáček, Štěpán; Lynnyk, Volodymyr; Rehák, Branislav
2020 - English
In the systems biology literature, complex systems of biochemical reactions (in form of ODEs) have become increasingly common. This issue of complexity is often making the modelled processes (e.g. drug metabolism, XME induction, DDI) difficult to intuit or to be computationally tractable, discouraging their practical use.
Keywords:
Dynamical system; Complex system; Optimization
Fulltext is available at external website.
Systems biology analysis of a drug metabolism (with slow-fast. . . )
In the systems biology literature, complex systems of biochemical reactions (in form of ODEs) have become increasingly common. This issue of complexity is often making the modelled processes (e.g. ...
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 ...
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 ...
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 ...
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 ...
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 ...
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 ...
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 ...
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 ...
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