**1473**

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**Two Algorithms for Risk-averse Reformulation of Multi-stage Stochastic Programming Problems**

Šmíd, Martin; Kozmík, Václav

2018 - English
Many real-life applications lead to risk-averse multi-stage stochastic problems, therefore effective solution of these problems is of great importance. Many tools can be used to their solution (GAMS, Coin-OR, APML or, for smaller problems, Excel), it is, however, mostly up to researcher to reformulate the problem into its deterministic equivalent. Moreover, such solutions are usually one-time, not easy to modify for different applications. We overcome these problems by providing a front-end software package, written in C++, which enables to enter problem definitions in a way close to their mathematical definition. Creating of a deterministic equivalent (and its solution) is up to the computer. In particular, our code is able to solve linear multi-stage with Multi-period Mean-CVaR or Nested Mean-CVaR criteria. In the present paper, we describe the algorithms, transforming these problems into their deterministic equivalents.
Keywords:
*Multi-stage stochastic programming; deterministic equivalent; multi-period CVaR; nested CVaR; optimization algorithm*
Fulltext is available at external website.
Two Algorithms for Risk-averse Reformulation of Multi-stage Stochastic Programming Problems

Many real-life applications lead to risk-averse multi-stage stochastic problems, therefore effective solution of these problems is of great importance. Many tools can be used to their solution (GAMS, ...

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**Problem of competing risks with covariates: Application to an unemployment study**

Volf, Petr

2018 - English
The study deals with the methods of statistical analysis in the situation of competing risks in the presence of regression. First, the problem of identification of marginal and joint distributions of competing random variables is recalled. The main objective is then to demonstrate that the parameters and, in particular, the correlation of competing variables, may depend on covariates. The approach is applied to solution of a real example with unemployment data. The model uses the Gauss copula and Cox’s regression model.
Keywords:
*statistical analysis; competing risks; unemployment study*
Fulltext is available at external website.
Problem of competing risks with covariates: Application to an unemployment study

The study deals with the methods of statistical analysis in the situation of competing risks in the presence of regression. First, the problem of identification of marginal and joint distributions of ...

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**Balancing Exploitation and Exploration via Fully Probabilistic Design of Decision Policies**

Kárný, Miroslav; Hůla, František

2018 - English
Adaptive decision making learns an environment model serving a design of a decision policy. The policy-generated actions influence both the acquired reward and the future knowledge. The optimal policy properly balances exploitation with exploration. The inherent dimensionality\ncurse of decision making under incomplete knowledge prevents the realisation of the optimal design.
Keywords:
*Exploitation; Exploration; Bayesian estimation; Adaptive systems; Fully probabilistic design; Kullback-Leibler divergence; Decision policy; Markov decision process*
Fulltext is available at external website.
Balancing Exploitation and Exploration via Fully Probabilistic Design of Decision Policies

Adaptive decision making learns an environment model serving a design of a decision policy. The policy-generated actions influence both the acquired reward and the future knowledge. The optimal policy ...

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**Dynamic Bayesian Networks for the Classification of Sleep Stages**

Vomlel, Jiří; Kratochvíl, Václav

2018 - English
Human sleep is traditionally classified into five (or six) stages. The manual classification is time consuming since it requires knowledge of an extensive set of rules from manuals and experienced experts. Therefore automatic classification methods appear useful for this task. In this paper we extend the approach based on Hidden Markov Models by relating certain features not only to the current time slice but also to the previous one. Dynamic Bayesian Networks that results from this generalization are thus capable of modeling features related to state transitions. Experiments on real data revealed that in this way we are able to increase the prediction accuracy.
Keywords:
*Dynamic Bayesian Network; Sleep Analysis*
Fulltext is available at external website.
Dynamic Bayesian Networks for the Classification of Sleep Stages

Human sleep is traditionally classified into five (or six) stages. The manual classification is time consuming since it requires knowledge of an extensive set of rules from manuals and experienced ...

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**Comparison of Shenoy’s Expectation Operator with Probabilistic Transforms and Perez’ Barycenter**

Jiroušek, R.; Kratochvíl, Václav

2018 - English
Shenoy’s paper published in this Proceedings of WUPES 2018 introduces an operator that gives instructions how to compute an expected value in the Dempster-Shafer theory of evidence. Up to now, there was no direct way to get the expected value of a utility function in D-S theory. If eeded, one had to find a probability mass function corresponding to the considered belief function, and then - using this probability mass function - to compute the classical probabilistic expectation. In this paper, we take four different approaches to defining probabilistic representatives of a belief function and compare which one yields to the best approximations of Shenoy’s expected values of various utility functions. The achieved results support our conjecture that there does not exist a probabilistic representative of a belief function that would yield the same expectations as the Shenoy’s new operator.
Keywords:
*expected utility; Dempster-Shafer theory; Shenoy's operator*
Fulltext is available at external website.
Comparison of Shenoy’s Expectation Operator with Probabilistic Transforms and Perez’ Barycenter

Shenoy’s paper published in this Proceedings of WUPES 2018 introduces an operator that gives instructions how to compute an expected value in the Dempster-Shafer theory of evidence. Up to now, there ...

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**Employing Bayesian Networks for Subjective Well-being Prediction**

Švorc, Jan; Vomlel, Jiří

2018 - English
This contribution aims at using Bayesian networks for modelling the relations between the individual subjective well-being (SWB) and the individual material situation. The material situation is approximated by subjective measures (perceived economic strain, subjective evaluation of the income relative to most people in the country and to own past) and objective measures (household’s income, material deprivation, financial problems and housing defects). The suggested Bayesian network represents the relations among SWB and the variables approximating the material situation. The structure is established based on the expertise gained from literature, whereas the parameters are learnt based on empirical data from 3rd edition of European Quality of Life Study for the Czech Republic, Hungary, Poland and Slovakia conducted in 2011. Prediction accuracy of SWB is tested and compared with two benchmark models whose structures are learnt using Gobnilp software and a greedy algorithm built in Hugin software. SWB prediction accuracy of the expert model is 66,83%, which is significantly different from no information rate of 55,16%. It is slightly lower than the two machine learnt benchmark models.
Keywords:
*Subjective well-being; Bayesian networks*
Fulltext is available at external website.
Employing Bayesian Networks for Subjective Well-being Prediction

This contribution aims at using Bayesian networks for modelling the relations between the individual subjective well-being (SWB) and the individual material situation. The material situation is ...

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**Proceedings of the 11th Workshop on Uncertainty Processing**

Kratochvíl, Václav; Vejnarová, Jiřina

2018 - English
The Workshop on Uncertainty Processing, better known under its abbreviation WUPES, celebrates its 30-year anniversary this year. In 1988, when the first Workshop took place, Czechoslovakia was still a communist country and a part of the Soviet bloc. Since then, many things have changed. For example, Czechoslovakia no longer exists as a country (because in 1993 it was peacefully split into two independent countries - Czechia and Slovakia). From this perspective, it is hard to believe that we have several participants who have attended most workshops in the the thirty-year history of WUPES. As of now, the Program Committee has accepted, based on the extended abstracts, 21 papers to be presented at the Workshop, and 19 out of them are to be published in the present Conference Proceedings. These papers cover diverse topics, such as information processing, decision making, and data analysis; but what is common to most of them is that they are related to uncertainty calculus - Bayesian Networks, Dempster-Shafer Theory, Belief Functions, Probabilistic Logic, Game Theory, etc.
Keywords:
*uncertainty processing; artificial intelligence; bayesian networks*
Fulltext is available at external website.
Proceedings of the 11th Workshop on Uncertainty Processing

The Workshop on Uncertainty Processing, better known under its abbreviation WUPES, celebrates its 30-year anniversary this year. In 1988, when the first Workshop took place, Czechoslovakia was still a ...

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**Representations of Bayesian Networks by Low-Rank Models**

Tichavský, Petr; Vomlel, Jiří

2018 - English
Conditional probability tables (CPTs) of discrete valued random variables may achieve high dimensions and Bayesian networks deﬁned as the product of these CPTs may become intractable by conventional methods of BN inference because of their dimensionality. In many cases, however, these probability tables constitute tensors of relatively low rank. Such tensors can be written in the so-called Kruskal form as a sum of rank-one components. Such representation would be equivalent to adding one artiﬁcial parent to all random variables and deleting all edges between the variables. The most difﬁcult task is to ﬁnd such a representation given a set of marginals or CPTs of the random variables under consideration. In the former case, it is a problem of joint canonical polyadic (CP) decomposition of a set of tensors. The latter ﬁtting problem can be solved in a similar manner. We apply a recently proposed alternating direction method of multipliers (ADMM), which assures that the model has a probabilistic interpretation, i.e., that all elements of all factor matrices are nonnegative. We perform experiments with several well-known Bayesian networks.\n\n
Keywords:
*canonical polyadic tensor decomposition; conditional probability tables; marginal probability tables*
Fulltext is available at external website.
Representations of Bayesian Networks by Low-Rank Models

Conditional probability tables (CPTs) of discrete valued random variables may achieve high dimensions and Bayesian networks deﬁned as the product of these CPTs may become intractable by conventional ...

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**Risk-sensitive and Mean Variance Optimality in Continuous-time Markov Decision Chains**

Sladký, Karel

2018 - English
In this note we consider continuous-time Markov decision processes with finite state and actions spaces where the stream of rewards generated by the Markov processes is evaluated by an exponential utility function with a given risk sensitivitycoefficient (so-called risk-sensitive models). If the risk sensitivity coefficient equals zero (risk-neutral case) we arrive at a standard Markov decision process. Then we can easily obtain necessary and sufficient mean reward optimality conditions and the variability can be evaluated by the mean variance of total expected rewards. For the risk-sensitive case, i.e. if the risk-sensitivity coefficient is non-zero, for a given value of the risk-sensitivity coefficient we establish necessary and sufficient optimality conditions for maximal (or minimal) growth rate of expectation of the exponential utility function, along with mean value of the corresponding certainty equivalent. Recall that in this case along with the total reward also its higher moments are taken into account.
Keywords:
*continuous-time Markov decision chains; exponential utility functions; certainty equivalent; mean-variance optimality; connections between risk-sensitive and risk-neutral optimality*
Fulltext is available at external website.
Risk-sensitive and Mean Variance Optimality in Continuous-time Markov Decision Chains

In this note we consider continuous-time Markov decision processes with finite state and actions spaces where the stream of rewards generated by the Markov processes is evaluated by an exponential ...

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**Platební regulační mechanismus jako zdroj zvyšování platů ve zdravotnictví**

Grim, Jiří

2018 - Czech
Princip zdravotního pojištění předpokládá, že se pacient v případě potřeby obrátí na lékaře, který mu poskytne odbornou pomoc, přičemž výkon lékaře, výdaje za léky a doplňující vyšetření proplácí zdravotní pojišťovna. Výsledkem je spontánní nárůst nákladů zdravotní péče u nás dobře známý z devadesátých let. Je zřejmé, že v systému, ve kterém o poskytnuté zdravotní péči musí rozhodovat lékaři v kontaktu s pacienty a její náklady následně hradí zdravotní pojišťovny, chybí záporná zpětná vazba, která by působila proti růstu nákladů. Důsledkem této hrubé systémové chyby je trvalý tlak na zvyšování výdajů za poskytnutou zdravotní péči a hrozící platební neschopnost nutí zdravotní pojišťovny zavádět regulační opatření k omezení růstu nákladů. The principle of health insurance presupposes that the patient will contact a doctor who will provide him / her with professional help, whereby the doctor, medical expenses and additional examinations are paid by the health insurance company. The result is a spontaneous increase in health care costs well-known in the nineties. It is clear that there is no negative feedback in the system where the healthcare provided must be made by doctors in contact with patients and its costs are being covered by health insurance companies. As a result of this gross systemic error, there is a continuing pressure to increase healthcare spending and imminent insolvency forces the health insurers to introduce regulatory measures to curb the cost increase.
Keywords:
*Medical care; finance; regulation mechanismus*
Fulltext is available at external website.
Platební regulační mechanismus jako zdroj zvyšování platů ve zdravotnictví

Princip zdravotního pojištění předpokládá, že se pacient v případě potřeby obrátí na lékaře, který mu poskytne odbornou pomoc, přičemž výkon lékaře, výdaje za léky a doplňující vyšetření proplácí ...

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