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By Prof. Dr. Jaromír Antoch (auth.), Prof. Dr. Jaromír Antoch (eds.)

Although no-one is, most likely, too enthused concerning the suggestion, it's a proven fact that the advance of such a lot empirical sciences to a good expand relies of the advance of knowledge research tools and strategies, which, as a result of necessity of purposes of desktops for that pur­ pose, really implies that it virtually is determined by the developments and orientation of computational records. This quantity comprises whole texts of the lectures held throughout the summer season institution on "Computational elements of version selection" orga­ nized together by means of Charles collage, Prague, and overseas Associa­ tion for Statistical Computing (IASC) on July 1-14, 1991, in Prague. major goals of the summer season tuition have been to study and examine many of the contemporary advancements bearing on computational facets of the version selection in addition to their theoretical heritage. the subjects covers the issues of the swap element detection, powerful estimation and its computational points, type utilizing binary bushes, stochastic ap­ proximation and optimization together with the dialogue approximately to be had software program, computational features of graphical version choice and mul­ tiple hypotheses trying out. The bridge among those diverse techniques is shaped by way of the survey paper approximately statistical functions of synthetic intelligence.

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Basseville M. , Detection of abrupt changes in signals and dynamical systems, Lecture Notes in Control and Information Sciences, vol. 77 (Basseville M. ), Springer- Verlag, New York, 1986. Bhattacharya G. K. and Johnson R. , Nonparametric tests for shift at an unknown time point, Annals of Mathematical Statistics 39 (1968), 1731-1743. Broemling L. D. , Econometrics and Structural Change, Serie Statistics, vol. 74, M. Dekker, New York, 1986. Brown R. , Durbin J. and Evans J. , Techniques for te"ting the con"tancy of regression relationships over time (with discussion), Journal of Royal Statistical Society B37 (1975), 149 -192.

Dekker, New York, 1986. Brown R. , Durbin J. and Evans J. , Techniques for te"ting the con"tancy of regression relationships over time (with discussion), Journal of Royal Statistical Society B37 (1975), 149 -192. , Nonparametric change -point estimation, Annals of Statistics 16 (1988),188-197. Chernoff H. , Estimating the current mean of a normal distribution which is subject to changes in time, Annals of Mathematical Statistics 35 (1964),999-1018. Chin Choy J. H. and Broemling L. , Some Bayesian inferences for achanging linear model, Technometries 22 (1980), 71-78.

We can assume that the "bad" observations follow another model generated by some distribution H. Such a situation may be described easily by the distributions from the model (1) :F~ = {F I F = (1 - c:)cI>B + c:H, H any distribution}, where c:, 0 ~ c: ~ 1, establishes the ratio of the "bad" observations (at least approximately). Model (1) is one of the most frequently used alternatives to the normal model :Fo and is usually called c:-contaminated normal model. For Hone uses, as a rule, the distribution with heavier tails than those of the normal one forming the "true" model.

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