Search Results - (( probable estimation methods algorithm ) OR ( data distribution a algorithm ))

Semiparametric inference procedure for the accelarated failure time model with interval-censored data by Karimi, Mostafa

“…A computationally simple two-step iterative algorithm, called estimationapproximation algorithm, is introduced for estimating the parameters of the model on the basis of the rank estimators. …”
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Estimation of Transformers Health Index Based on Condition Parameter Factor and Hidden Markov Model by Selva A.M., Yahaya M.S., Azis N., Ab Kadir M.Z.A., Jasni J., Yang Ghazali Y.Z.

Some families of count distributions for modelling zero-inflation and dispersion / Low Yeh Ching by Low, Yeh Ching

“…A popular distribution for the modelling of discrete count data is the Poisson distribution. …”
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Determination of dengue hemorrhagic fever disease factors using neural network and genetic algorithms / Yuliant Sibaroni, Sri Suryani Prasetiyowati and Iqbal Bahari Sudrajat by Yuliant, Sibaroni, Sri Suryani, Prasetiyowati, Iqbal Bahari, Sudrajat

“…Determination of the best factor is carried out in a genetic algorithm by combining several parameters of the crossover probability (Pc) and mutation probability (Pm). …”
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Parameter estimation of K-distributed sea clutter based on fuzzy inference and Gustafson-Kessel clustering by Davari, Atefeh, Marhaban, Mohammad Hamiruce, Mohd Noor, Samsul Bahari, Karimadini, Mohammad, Karimoddini, Ali

“…The main contribution of the proposed method is the ability to estimate the parameters, given a small number of data which will usually be the case in practical applications. …”
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Estimation of transformers health index based on condition parameter factor and hidden Markov model by Mohd Selva, Amran, Yahaya, Muhammad Sharil, Azis, Norhafiz, Ab Kadir, Mohd Zainal Abidin, Jasni, Jasronita, Yang Ghazali, Young Zaidey

“…Subsequently, the future states probability distribution was computed based on the HMM prediction model and viterbi algorithm was applied to find the best optimal path sequence of HI for the respective observable condition. …”
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Parameter estimation and outlier detection for some types of circular model / Siti Zanariah binti Satari by Satari, Siti Zanariah

“…We first look at the concentration parameter of von Mises distribution. The von Mises distribution is the most commonly used probability distribution of a circular random variable, and the concentration of a circular data set is measured using the mean resultant length. …”
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New learning algorithm based on Hidden Markov Model (HMM) as stochastic modelling for pattern calssification by Balakrishnan, Malarvili, Ting, Chee Ming, Shaikh Salleh, Sheikh Hussain

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Parameter estimation of Kumaraswamy Burr type X models based on cure models with or without covariates by Yusuf, Madaki Umar

“…Hence, to obtain effective results for the cure models with censored data and covariates, the estimation of the parameters was done under a Bayesian approach using G-S method. …”
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Robust Kernel Density Function Estimation by Dadkhah, Kourosh

“…The results of the study indicate that the proposed methods are capable of labelling normal observation and potential outliers in a data set. …”
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An impulsive noise analyser using amplitude probability distribution (APD) for broadband-wired communication by Zainal Abidin, Ahmadun Nijar

“…It is vital to have correct measurement set-up, signal power level, sampling rate, sample points and filter characterisation in order to acquire accurate data representation of the noise patterns. The APD graph is generated by the analyser using the APD algorithm method which employs the envelope sampling technique from actual probability. …”
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Probabilistic load flow�based optimal placement and sizing of distributed generators by Hossain F.A., Rokonuzzaman M., Amin N., Zhang J., Mishu M.K., Tan W.-S., Islam M.R., Roy R.B.

Transformer asset management based on Markov Prediction Model utilizing health index by Yahaya, Muhammad Sharil

“…Further, the future performance condition curve of the transformers was determined based on the Markov chain algorithm. A statistical analysis was carried out to test the performance of MPM on the HI data. …”
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Solving single and bi-objective surgery scheduling problems using local search heuristic by Ab Rashid, Nur Shafiqah

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Power prediction using the wind turbine power curve and data-driven approaches / Ehsan Taslimi Renani by Ehsan Taslimi , Renani

“…To evaluate the performance of the Weibull parameters’ estimator methods, two sets of data are considered, one based on simulated data with different random variable size and the other based on actual data collected from a wind farm in Iran. …”
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Sizing and placement of battery-coupled distributed photovoltaic generations by Ali, A., Mohd Nor, N., Ibrahim, T., Fakhizan Romlie, M.

“…Mixed-integer optimization using a genetic algorithm is employed for solving the optimization problem. …”
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Class binarization with self-adaptive algorithm to improve human activity recognition by Zainudin, Muhammad Noorazlan Shah

“…To enhance the selection of most highly ranking features, irrelevant features are ‘pruned’ based on determined boundary threshold. In order to estimate the quality of ‘pruned’ features, self-adaptive DE algorithm is proposed. …”
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Modelling and Forecasting the Kuala Lumpur Composite Index Rate of Returns Using Generalised Autoregressive Conditional Heteroscedasticity Models by Abdul Muthalib, Maiyastri

“…The EM algorithm is applied to split the heterogeneous data, and the estimated parameters are used to correct the outlying data using the Mahalanobis Distance. …”
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Comparative Analysis of Artificial Intelligence Methods for Streamflow Forecasting by YAXING, WEI, HUZAIFA, HASHIM, Lai, Sai Hin, CHONG, KAI LUN, HUANG, YUK FENG, ALI NAJAH, AHMED, MOHSEN, SHERIF, AHMED, EL-SHAFIE

“…Convolutional neural networks exhibit lower stochasticity than artificial neural networks when dealing with complex time series data and with data transformed into a format suitable for modeling. …”
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Simulation algorithm of bayesian approach for choice-conjoint model by Zulhanif

“…Therefore this research propose simulation algorithm of Bayesian approach for estimating parameter in MPM by Bayesian analysis to avoid computational difficulties in computing the maximum likelihood estimates (MLE).…”
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