Monte Carlo experiments are an efficient tool for investigation of the Laser-Induced Damage Threshold (LIDT) testing with pulsed lasers. In this study, the approach of sequential Monte Carlo search is developed for LIDT testing with bundle of laser pulses and compared with the approach of Sample Average Approximation (SAA). The likelihood ratio test is applied to accept or reject the hypothesis about the data distribution.
This paper proposes methodology for companies’ assessment. There is suggesting assessing the company’s prospect, not only according to share price, forecasts of the analysts, but also on the basis of position of each company in two-dimensional space in respect of the other companies. Seeking to describe the share prices of a company during the year, the parameters of skew t distribution are calculated. Then they are used in the inputs of random forest algorithm. Proximity matrices are stored during classification, and they are displayed in two-dimensional space. Thus, two clusters are obtained: one of the companies with upgrade trend, another one – with downgrade trend. This method may be useful those investors who are important to choose the most promising companies of all industry without wasting a lot of time.
This work contains Monte–Carlo Markov Chain algorithm for estimation of multi-dimensional rare events frequencies. Logits of rare event likelihood we are modeling with Poisson distribution, which parameters are distributed by multivariate normal law with unknown parameters – mean vector and covariance matrix. The estimations of unknown parameters are calculated by the maximum likelihood method. There are equations derived, those must be satisfied with model’s maximum likelihood parameters estimations. Positive definition of evaluated covariance matrixes are controlled by calculating ratio between matrix maximum and minimum eigenvalues.