A Monte Carlo approach for maximum power estimation based on extreme value theory

2002 ◽  
Vol 21 (4) ◽  
pp. 415-432 ◽  
Author(s):  
N.E. Evmorfopoulos ◽  
G.I. Stamoulis ◽  
J.N. Avaritsiotis
Keyword(s):  
Monte Carlo ◽  
Extreme Value Theory ◽  
Power Estimation ◽  
Value Theory ◽  
Extreme Value ◽  
Maximum Power ◽  
Monte Carlo Approach

scholarly journals Extreme Value Theory Applied to r Largest Order Statistics Under the Bayesian Approach

2019 ◽  
Vol 42 (2) ◽  
pp. 143-166 ◽  
Author(s):  
Renato Santos Silva ◽  
Fernando Ferraz Nascimento
Keyword(s):  
Monte Carlo ◽  
Order Statistics ◽  
Bayesian Approach ◽  
Extreme Value Theory ◽  
Likelihood Estimation ◽  
Value Theory ◽  
Extreme Value ◽  
Environmental Data ◽  
Heavy Tailed ◽  
The Bayesian Approach

Extreme Value Theory (EVT) is an important tool to predict efficient gains and losses. Its main areas of analyses are economic and environmental. Initially, for that form of event, it was developed the use of patterns of parametric distribution such as Normal and Gamma. However, economic and environmental data presents, in most cases, a heavy-tailed distribution, in contrast to those distributions. Thus, it was faced a great difficult to frame extreme events. Furthermore, it was almost impossible to use conventional models, making predictions about non-observed events, which exceed the maximum of observations. In some situations EVT is used to analyse only the maximum of some dataset, which provide few observations, and in those cases it is more effective to use the r largest-order statistics. This paper aims to propose Bayesian estimators' for parameters of the r largest-order statistics. During the research, it was used Monte Carlo simulation to analyze the data, and it was observed some properties of those estimators, such as mean, variance, bias and Root Mean Square Error (RMSE). The estimation of the parameters provided inference for its parameters and return levels. This paper also shows a procedure to the choice of the r-optimal to the r largest-order statistics, based on the Bayesian approach applying Markov chains Monte Carlo (MCMC). Simulation results reveal that the Bayesian approach has a similar performance to the Maximum Likelihood Estimation, and the applications were developed using the Bayesian approach and showed a gain in accurary compared with otherestimators.

scholarly journals A New Statistical Method for Maximum Power Estimation in CMOS VLSI Circuits

2000 ◽  
Vol 22 (3) ◽  
pp. 215-233 ◽  
Author(s):  
N. E. Evmorfopoulos ◽  
J. N. Avaritsiotis
Keyword(s):  
Probability Distribution ◽  
Power Consumption ◽  
Extreme Value Theory ◽  
Power Estimation ◽  
Value Theory ◽  
Vlsi Circuits ◽  
Maximum Power ◽  
Instantaneous Power ◽  
Simplifying Assumptions ◽  
Cmos Vlsi

A method for maximum power estimation in CMOS VLSI circuits is proposed. The method is based on extreme value theory and allows for the calculation of the upper end point of the probability distribution which is followed by the instantaneous power drawn from the supply bus. The main features of the method are the relatively small and circuitin-dependent subset of input patterns required for accurate prediction of maximum power and its simulative nature which ensures that no over-simplifying assumptions are made. Application of the proposed method to eight distributions, which come close to the behavior of power consumption in VLSI circuits, proved its superior capabilities with respect to existing methods.

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