Bernoulli Distribution

2005 ◽  
pp. 43-48
Keyword(s):  
Bernoulli Distribution

Triangulation between Bernoulli Distribution and Laplacian Autoregressive Model to Predict Probability of Increase in Stock Price

2021 ◽  
Vol 9 (4) ◽  
pp. 588-593
Author(s):  
Suparman Suparman ◽  
A. M. Diponegoro ◽  
Mahyudin Ritonga ◽  
Yahya Hairun ◽  
Tedy Machmud ◽  
...  
Keyword(s):  
Autoregressive Model ◽  
Stock Price ◽  
Bernoulli Distribution

scholarly journals Passivity of Memristive BAM Neural Networks with Probabilistic and Mixed Time-Varying Delays

2018 ◽  
Vol 2018 ◽  
pp. 1-25
Author(s):  
Weiping Wang ◽  
Meiqi Wang ◽  
Xiong Luo ◽  
Lixiang Li ◽  
Wenbing Zhao
Keyword(s):  
Neural Networks ◽  
Probability Distribution ◽  
Linear Matrix ◽  
Time Varying ◽  
Bam Neural Networks ◽  
Bernoulli Distribution ◽  
Matrix Inequalities ◽  
Bidirectional Associative Memory ◽  
Leakage Delays ◽  
Numerical Examples

This paper is concerned with the passivity problem of memristive bidirectional associative memory neural networks (MBAMNNs) with probabilistic and mixed time-varying delays. By applying random variables with Bernoulli distribution, the information of probability time-varying delays is taken into account. Furthermore, we consider the probability distribution of the variation and the extent of the delays; therefore, the results derived are less conservative than in the existing papers. In particular, the leakage delays as well as distributed delays are all taken into consideration. Based on appropriate Lyapunov-Krasovskii functionals (LKFs) and some useful inequalities, several conditions for passive performance are established in linear matrix inequalities (LMIs). Finally, numerical examples are given to demonstrate the feasibility of the presented theories, and the results reveal that the probabilistic and mixed time-varying delays have an unstable influence on the system and should not be ignored.

Nonclassicality of photon-modulated spin coherent states in the Holstein-Primakoff realization

2021 ◽  
Author(s):  
Xiaoyan Zhang ◽  
Jisuo Wang ◽  
Lei Wang ◽  
Xiangguo Meng ◽  
Baolong Liang
Keyword(s):  
Coherent States ◽  
Wigner Distribution ◽  
Hermite Polynomials ◽  
Photon Number ◽  
Spin Ladder ◽  
Bernoulli Distribution ◽  
Ladder Operators ◽  
Spin Number ◽  
Photon Number Distribution ◽  
Spin Coherent States

Abstract Two new photon-modulated spin coherent states (SCSs) are introduced by operating the spin ladder operators J ± on the ordinary SCS in the Holstein-Primakoff realization and the nonclassicality is exhibited via their photon number distribution, second-order correlation function, photocount distribution and negativity of Wigner distribution. Analytical results show that the photocount distribution is a Bernoulli distribution and the Wigner functions are only associated with two-variable Hermite polynomials. Compared with the ordinary SCS, the photon-modulated SCSs exhibit more stronger nonclassicality in certain regions of the photon modulated number k and spin number j, which means that the nonclassicality can be enhanced by selecting suitable parameters.

scholarly journals Stationary Threshold Vector Autoregressive Models

2018 ◽  
Vol 11 (3) ◽  
pp. 45 ◽  
Author(s):  
Galyna Grynkiv ◽  
Lars Stentoft
Keyword(s):  
Steady State ◽  
Stationary Distribution ◽  
Sufficient Conditions ◽  
Threshold Models ◽  
Bernoulli Distribution ◽  
Vector Autoregressive Model ◽  
Economic Data ◽  
Vector Autoregressive Models ◽  
Vector Autoregressive ◽  
Necessary And Sufficient

This paper examines the steady state properties of the Threshold Vector Autoregressive model. Assuming that the trigger variable is exogenous and the regime process follows a Bernoulli distribution, necessary and sufficient conditions for the existence of stationary distribution are derived. A situation related to so-called “locally explosive models”, where the stationary distribution exists though the model is explosive in one regime, is analysed. Simulations show that locally explosive models can generate some of the key properties of financial and economic data. They also show that assessing the stationarity of threshold models based on simulations might well lead to wrong conclusions.

scholarly journals A Median-Based Machine-Learning Approach for Predicting Random Sampling Bernoulli Distribution Parameter

2019 ◽  
Vol 06 (01) ◽  
pp. 17-28 ◽  
Author(s):  
Hoang Pham ◽  
David H. Pham
Keyword(s):  
Machine Learning ◽  
Population Distribution ◽  
Real Life ◽  
Population Data ◽  
Correction Method ◽  
Machine Learning Techniques ◽  
Learning Approach ◽  
Large Sample Size ◽  
Bernoulli Distribution ◽  
Machine Learning Approach

In real-life applications, we often do not have population data but we can collect several samples from a large sample size of data. In this paper, we propose a median-based machine-learning approach and algorithm to predict the parameter of the Bernoulli distribution. We illustrate the proposed median approach by generating various sample datasets from Bernoulli population distribution to validate the accuracy of the proposed approach. We also analyze the effectiveness of the median methods using machine-learning techniques including correction method and logistic regression. Our results show that the median-based measure outperforms the mean measure in the applications of machine learning using sampling distribution approaches.

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