Aalto University, P.O. Box 11000, FI-00076 Aalto www.aalto.fi Author Jirka Poropudas Name of the doctoral dissertation Bayesian Networks, Influence Diagrams, and Games in Simulation Metamodeling Publisher Aalto University Unit School of Science, Department of Mathematics and Systems Analysis Series Aalto University publication series DOCTORAL DISSERTATIONS 76/2011 Field of research Systems and ...

The quality of Support Vector Machines (SVM) binary classification of spatial environmental data is evaluated with geostatistical nonparametric conditional stochastic simulations a spatial Monte Carlo model based on sequential indicator simulation algorithm. Equally probable realizations are generated and compared with SVM classification. Uncertainty of predictions is described by conditional s...

We consider nonparametric estimation of conditional medians for time series data. The time series data are generated from two mutually independent linear processes. The linear processes may show long-range dependence. The estimator of the conditional medians is based on minimizing the locally weighted sum of absolute deviations for local linear regression. We present the asymptotic distribution...

We consider nonparametric estimation of the conditional qth quantile for stationary time series. We deal with stationary time series with strong time dependence and heavy tails under the setting of random design. We estimate the conditional qth quantile by local linear regression and investigate the asymptotic properties. It is shown that the asymptotic properties are affected by both the time ...

Abstract—An expectation-maximization (EM) algorithm for independent component analysis in the presence of gaussian noise is presented. The estimation of the conditional moments of the source posterior can be accomplished by maximum a posteriori estimation. The approximate conditional moments enable the development of an EM algorithm for inferring the most probable sources and learning the param...

in this paper the conditional location problem is discussed. conditional location problems have a wide range of applications in location science. a new meta-heuristic algorithm for solving conditional p-median problems is proposed and results are compared to those of the previous studies. this algorithm produces much better results than the previous formulations.

We develop and evaluate a two-level simulation procedure that produces a confidence interval for expected shortfall. The outer level of simulation generates financial scenarios, whereas the inner level estimates expected loss conditional on each scenario. Our procedure uses the statistical theory of empirical likelihood to construct a confidence interval. It also uses tools from the ranking-and...

In this paper, we propose some algorithms for the simulation of the distribution of certain diffusions conditioned on terminal point. We prove that the conditional distribution is absolutely continuous with respect to the distribution of another diffusion which is easy for simulation, and the formula for the density is given explicitly.

Conditional Monte Carlo with intermediate estimations is a proposal for applying Conditional Monte Carlo to the estimation of the failure probability of a multicomponent system modeled by a Markov chain. Through this probability it is possible to compute the MTTF. This article introduces the basis of this method, proves that it is unbiased, that its variance is less than the standard simulation...

This paper presents a quasi-conditional likelihood method for the consistent estimation of both continuous and count data models with excess zeros and unobserved individual heterogeneity when the true data generating process is unknown. Monte Carlo simulation studies show that our zero-inflated quasi-conditional maximum likelihood (ZI-QCML) estimator outperforms other methods and is robust to d...