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National Repository of Grey Literature 3 records found  Search took 0.01 seconds. 
Expectation-Maximization Algorithm
 Vichr, Jaroslav ; Pešta, Michal (advisor) ; Zvára, Karel (referee)
EM (Expectation-Maximization) algorithm is an iterative method for finding maximum likelihood estimates in cases, when either complete data include missing values or assuming the existence of additional unobserved data points can lead to more simple formulation of the model. Each of its iterations consists of two parts. During the E step (expectation) we calculate the expected value of the log-likelihood function of the complete data, with respect to the observed data and the current estimate of the parameter. The M step (maximization) then finds new estimate, which will maximize the function obtained in the previous step and which will be used in the next iteration in step E. EM algorithm has important use in e.g. price and manage risk of the portfolio.
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Econometric models for Czech insurance market
 Vichr, Jaroslav ; Cipra, Tomáš (advisor) ; Pešta, Michal (referee)
Relationships between insurance variables representing the cash flows of the Czech insurance market can be effectively modeled using a dynamic system of linear simultaneous equations. The source of the underlying data to build such a model can be publicly available annual reports of the Czech Insurance Association. The resulting model can find its use mainly to predict the future development of financial flows based on historical observations and analysis of possible scenarios. It is this analysis of potential projections and their consequences which provides insight into how e.g. a future decrease of new insurance policies would affect the expected amount of claims costs and the volume of written premiums.
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Expectation-Maximization Algorithm
 Vichr, Jaroslav ; Pešta, Michal (advisor) ; Zvára, Karel (referee)
EM (Expectation-Maximization) algorithm is an iterative method for finding maximum likelihood estimates in cases, when either complete data include missing values or assuming the existence of additional unobserved data points can lead to more simple formulation of the model. Each of its iterations consists of two parts. During the E step (expectation) we calculate the expected value of the log-likelihood function of the complete data, with respect to the observed data and the current estimate of the parameter. The M step (maximization) then finds new estimate, which will maximize the function obtained in the previous step and which will be used in the next iteration in step E. EM algorithm has important use in e.g. price and manage risk of the portfolio.
Detailed record

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1 VICHR, Jan
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