**Acknowledgement**

This research was supported in part by Grant No. 06.1936, Grant No. 07.2036, Grant No. 09.1014, and Grant No. 09.1544 from the Latvian Council of Science and the National Institute of Mathematics and Informatics of Latvia.

#### **6. References**

208 Stochastic Modeling and Control

and the proof is complete. □

airline seat inventory control.

and small data samples

distributions.

**Author details** 

*University of Latvia, Latvia* 

**Acknowledgement** 

Nicholas A. Nechval and Maris Purgailis

Institute of Mathematics and Informatics of Latvia.

**5. Conclusions and directions for future research** 

probability interpretations in finite samples.

*Remark 2.* If l=m, then the result of Theorem 5 can be used to construct the dynamic policy of

In this paper, we develop a new frequentist approach to improve predictive statistical decisions for revenue optimization problems under parametric uncertainty of the underlying distributions for the customer demand. Frequentist probability interpretations of the methods considered are clear. Bayesian methods are not considered here. We note, however, that, although subjective Bayesian prediction has a clear personal probability interpretation, it is not generally clear how this should be applied to non-personal prediction or decisions. Objective Bayesian methods, on the other hand, do not have clear

For constructing the improved statistical decisions, a new technique of invariant embedding of sample statistics in a performance index is proposed. This technique represents a simple and computationally attractive statistical method based on the constructive use of the invariance principle in mathematical statistics. The method used is that of the invariant embedding of sample statistics in a performance index in order to form pivotal quantities, which make it possible to eliminate unknown parameters (i.e., parametric uncertainty) from the problem. It is especially efficient when we deal with asymmetric performance indexes

More work is needed, however, to obtain improved or optimal decision rules for the problems of unconstrained and constrained optimization under parameter uncertainty when: (i) the observations are from general continuous exponential families of distributions, (ii) the observations are from discrete exponential families of distributions, (iii) some of the observations are from continuous exponential families of distributions and some from discrete exponential families of distributions, (iv) the observations are from multiparametric or multidimensional distributions, (v) the observations are from truncated distributions, (vi) the observations are censored, (vii) the censored observations are from truncated

This research was supported in part by Grant No. 06.1936, Grant No. 07.2036, Grant No. 09.1014, and Grant No. 09.1544 from the Latvian Council of Science and the National


*Engineering and Computer Science: Proceedings of the World Congress on Engineering (WCE 2011)*, pp. 865--871. London, U.K.

**Chapter 11** 

© 2012 Šimović et al., licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

© 2012 Šimović et al., licensee InTech. This is a paper distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

**Stochastic Based Simulations and Measurements** 

**of Some Objective Parameters of Acoustic** 

**Acoustic Quality with Acoustics Optimization** 

This chapter offers an original scientific research about stochastic based simulations and measurements of some objective parameters of acoustic quality and subjective evaluation of room acoustic quality, with acoustics optimization in multimedia classroom, complete scientific analysis, and application. The organisation of this chapter is very simple. After a short introduction that is an overview of completed research there are two main chapter parts with some definitions and explanations on the main subjects and research done. The conclusion to this research is contained in the final chapter segment, before the cited

According to http://www.answers.com/topic/convolution (2011/12/31), stochastic control methodology (SCM) is applied in a variety of fields including the computing, communications and acoustics optimization in multimedia environment. Also, SCT as a branch of control theory (CT) that deals with systems which involve random variables/signals and which occurrence can only be described in probabilistic terms attempts to predict and minimize the effect of these random signals through the optimization of controller design for acoustics optimization in multimedia room. Such deviations occur when random noise and disturbance processes are present in a control system, so that the system does not follow its prescribed course but deviates from the latter by a randomly

**Quality: Subjective Evaluation of Room** 

**in Multimedia Classroom** 

**(Analysis with Application)** 

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/45950

**1. Introduction** 

references.

Vladimir Šimović, Siniša Fajt and Miljenko Krhen


Vladimir Šimović, Siniša Fajt and Miljenko Krhen

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/45950
