scholarly journals Opinion mining for user experience evaluation model using kernel-naive bayes classification algorithm

2021 ◽  
Vol 9 ◽  
Author(s):  
Rajkumar P ◽  
◽  
Kogilavani S.V ◽  
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
User Experience ◽  
Opinion Mining ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Classification Algorithm ◽  
Mcmc Method ◽  
Ux Design

User experience evaluation approach is the major key to adapt the new trends and technology. The product launch is based on the various opinions of users and availability of product. The first impression about the product makes successful sales, which is analysed with UX (User eXperience) design. Before developing / launching the product, have to evaluate the user experience model by online sources. The opinion/sentimental analysis are the way to capture the people’s opinion about the product. Rating, page session, website page views, and number of buyers or users are evaluated as a graph model and predict the requirement of the product. This process makes the product’s benefits. The previous work utilizes the Markov Chain Monte Carlo (MCMC) Method to model the UX design. In this proposed research work, the opinion mining approach is used to get the dataset from Google analytics. This dataset is model using Kernel based Naïve Bayes Classification algorithm and the prior & posterior probability is calculated by MCMC (Markov Chain Monte Carlo) techniques. Classification approach takes the training and testing data. Here the confusion matrix is used to create the UX evaluation model’s accuracy. By this proposed algorithm, it summarized the positive and negative opinion then we can calculate the accuracy of the system and it easily identifies the user opinion. This proposed UX design model improves the result as compared to the previous MCMC method. The data mining based sentimental classification is done with the help of MATLAB 2018a tool.

scholarly journals Measuring Stellar Radial Velocity using Markov Chain Monte Carlo(MCMC) Method

2013 ◽  
Vol 9 (S298) ◽  
pp. 441-441
Author(s):  
Yihan Song ◽  
Ali Luo ◽  
Yongheng Zhao
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Probability Distribution ◽  
Radial Velocity ◽  
Mcmc Methods ◽  
Mcmc Method ◽  
Stellar Spectra ◽  
Mcmc Simulation ◽  
Template Fitting

AbstractStellar radial velocity is estimated by using template fitting and Markov Chain Monte Carlo(MCMC) methods. This method works on the LAMOST stellar spectra. The MCMC simulation generates a probability distribution of the RV. The RV error can also computed from distribution.

scholarly journals IMPLEMENTASI METODE MARKOV CHAIN MONTE CARLO DALAM PENENTUAN HARGA KONTRAK BERJANGKA KOMODITAS

2015 ◽  
Vol 4 (3) ◽  
pp. 122
Author(s):  
PUTU AMANDA SETIAWANI ◽  
KOMANG DHARMAWAN ◽  
I WAYAN SUMARJAYA
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Simulation Method ◽  
Commodity Price ◽  
Future Contract ◽  
Mcmc Method ◽  
Futures Contract ◽  
Mcmc Simulation ◽  
Hit And Run

The aim of the research is to implement Markov Chain Monte Carlo (MCMC) simulation method to price the futures contract of cocoa commodities. The result shows that MCMC is more flexible than Standard Monte Carlo (SMC) simulation method because MCMC method uses hit-and-run sampler algorithm to generate proposal movements that are subsequently accepted or rejected with a probability that depends on the distribution of the target that we want to be achieved. This research shows that MCMC method is suitable to be used to simulate the model of cocoa commodity price movement. The result of this research is a simulation of future contract prices for the next three months and future contract prices that must be paid at the time the contract expires. Pricing future contract by using MCMC method will produce the cheaper contract price if it compares to Standard Monte Carlo simulation.

Efficient History Matching Using the Markov-Chain Monte Carlo Method by Means of the Transformed Adaptive Stochastic Collocation Method

SPE Journal ◽  
2019 ◽  
Vol 24 (04) ◽  
pp. 1468-1489 ◽  
Author(s):  
Qinzhuo Liao ◽  
Lingzao Zeng ◽  
Haibin Chang ◽  
Dongxiao Zhang
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Collocation Method ◽  
Posterior Distribution ◽  
History Matching ◽  
Stochastic Collocation ◽  
Mcmc Method ◽  
Stochastic Collocation Method ◽  
Surrogate System

Summary Bayesian inference provides a convenient framework for history matching and prediction. In this framework, prior knowledge, system nonlinearity, and measurement errors can be directly incorporated into the posterior distribution of the parameters. The Markov-chain Monte Carlo (MCMC) method is a powerful tool to generate samples from the posterior distribution. However, the MCMC method usually requires a large number of forward simulations. Hence, it can be a computationally intensive task, particularly when dealing with large-scale flow and transport models. To address this issue, we construct a surrogate system for the model outputs in the form of polynomials using the stochastic collocation method (SCM). In addition, we use interpolation with the nested sparse grids and adaptively take into account the different importance of parameters for high-dimensional problems. Furthermore, we introduce an additional transform process to improve the accuracy of the surrogate model in case of strong nonlinearities, such as a discontinuous or unsmooth relation between the input parameters and the output responses. Once the surrogate system is built, we can evaluate the likelihood with little computational cost. Numerical results demonstrate that the proposed method can efficiently estimate the posterior statistics of input parameters and provide accurate results for history matching and prediction of the observed data with a moderate number of parameters.

scholarly journals On the Markov Chain Monte Carlo (MCMC) method

Sadhana ◽  
2006 ◽  
Vol 31 (2) ◽  
pp. 81-104 ◽  
Author(s):  
Rajeeva L. Karandikar
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Mcmc Method

scholarly journals Analyzing statistical age models to determine the equivalent dose and burial age using a Markov chain Monte Carlo method

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Jun Peng
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Likelihood Estimation ◽  
Equivalent Dose ◽  
Mcmc Method ◽  
Minimum Age ◽  
Age Model ◽  
Multiple Samples ◽  
Age Estimates

AbstractIn optically stimulated luminescence (OSL) dating, statistical age models for equivalent dose (De) distributions are routinely estimated using the maximum likelihood estimation (MLE) method. In this study, a Markov chain Monte Carlo (MCMC) method was used to analyze statistical age models, including the central age model (CAM), the minimum age model (MAM), the maximum age model (MXAM), etc. This method was first used to obtain sampling distributions on parameters of interest in an age model using De distributions from individual sedimentary samples and subsequently extended to simultaneously extract age estimates from multiple samples with stratigraphic constraints. The MCMC method allows for the use of Bayesian inference to refine chronological sequences from multiple samples, including both fully and partially bleached OSL dates. This study designed easily implemented open-source numeric programs to perform MCMC sampling. Measured and simulated De distributions are used to validate the reliability of dose (age) estimates obtained by this method. Findings from this study demonstrate that estimates obtained by the MCMC method can be used to informatively compare results obtained by the MLE method. The application of statistical age models to multiple OSL dates with stratigraphic orders using the MCMC method may significantly improve both the precision and accuracy of burial ages.

scholarly journals Preliminary Evaluation of Diagnostic Tests for Avian Influenza Using the Markov Chain Monte Carlo (MCMC) Method in an Emergency Surveillance

2007 ◽  
Vol 69 (6) ◽  
pp. 673-675 ◽  
Author(s):  
Takehisa YAMAMOTO ◽  
Toshiyuki TSUTSUI ◽  
Akiko NISHIGUCHI ◽  
Sota KOBAYASHI ◽  
Kenji TSUKAMOTO ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Avian Influenza ◽  
Diagnostic Tests ◽  
Preliminary Evaluation ◽  
Mcmc Method

Multimodal Markov chain Monte Carlo method for nonlinear petrophysical seismic inversion

Geophysics ◽  
2019 ◽  
Vol 84 (5) ◽  
pp. M1-M13 ◽  
Author(s):  
Leandro Passos de Figueiredo ◽  
Dario Grana ◽  
Mauro Roisenberg ◽  
Bruno B. Rodrigues
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Posterior Distribution ◽  
Seismic Inversion ◽  
Forward Model ◽  
Model Parameters ◽  
Petrophysical Properties ◽  
Mcmc Method ◽  
Data Set

One of the main objectives in the reservoir characterization is estimating the rock properties based on seismic measurements. We have developed a stochastic sampling method for the joint prediction of facies and petrophysical properties, assuming a nonparametric mixture prior distribution and a nonlinear forward model. The proposed methodology is based on a Markov chain Monte Carlo (MCMC) method specifically designed for multimodal distributions for nonlinear problems. The vector of model parameters includes the facies sequence along the seismic trace as well as the continuous petrophysical properties, such as porosity, mineral fractions, and fluid saturations. At each location, the distribution of petrophysical properties is assumed to be multimodal and nonparametric with as many modes as the number of facies; therefore, along the seismic trace, the distribution is multimodal with the number of modes being equal to the number of facies power the number of samples. Because of the nonlinear forward model, the large number of modes and as a consequence the large dimension of the model space, the analytical computation of the full posterior distribution is not feasible. We then numerically evaluate the posterior distribution by using an MCMC method in which we iteratively sample the facies, by moving from one mode to another, and the petrophysical properties, by sampling within the same mode. The method is extended to multiple seismic traces by applying a first-order Markov chain that accounts for the lateral continuity of the model properties. We first validate the method using a synthetic 2D reservoir model and then we apply the method to a real data set acquired in a carbonate field.

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