Simulation of the Energy Efficiency Auction Prices via the Markov Chain Monte Carlo Method

Over the years, electricity consumption behavior in Brazil has been analyzed due to financial and social problems. In this context, it is important to simulate energy prices of the energy efficiency auctions in the Brazilian electricity market. The Markov Chain Monte Carlo (MCMC) method generated simulations; thus, several samples were generated with different sizes. It is possible to say that the larger the sample, the better the approximation to the original data. Then, the Kernel method and the Gaussian mixture model used to estimate the density distribution of energy price, and the MCMC method were crucial in providing approximations of the original data and clearly analyzing its impact. Next, the behavior of the data in each histogram was observed with 500, 1000, 5000 and 10,000 samples, considering only one scenario. The sample which best approximates the original data in accordance with the generated histograms is the 10,000th sample, which consistently follows the behavior of the data. Therefore, this paper presents an approach to generate samples of auction energy prices in the energy efficiency market, using the MCMC method through the Metropolis–Hastings algorithm. The results show that this approach can be used to generate energy price samples.

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Gaussian mixture Markov chain Monte Carlo method for linear seismic inversion

Geophysics ◽

10.1190/geo2018-0529.1 ◽

2019 ◽

Vol 84 (3) ◽

pp. R463-R476 ◽

Cited By ~ 11

Author(s):

Leandro Passos de Figueiredo ◽

Dario Grana ◽

Mauro Roisenberg ◽

Bruno B. Rodrigues

Keyword(s):

Monte Carlo ◽

Markov Chain ◽

Markov Chain Monte Carlo ◽

Elastic Properties ◽

Posterior Distribution ◽

Seismic Data ◽

Gaussian Mixture ◽

Gaussian Component ◽

Well Test ◽

Mcmc Method

We have developed a Markov chain Monte Carlo (MCMC) method for joint inversion of seismic data for the prediction of facies and elastic properties. The solution of the inverse problem is defined by the Bayesian posterior distribution of the properties of interest. The prior distribution is a Gaussian mixture model, and each component is associated to a potential configuration of the facies sequence along the seismic trace. The low frequency is incorporated by using facies-dependent depositional trend models for the prior means of the elastic properties in each facies. The posterior distribution is also a Gaussian mixture, for which the Gaussian component can be analytically computed. However, due to the high number of components of the mixture, i.e., the large number of facies configurations, the computation of the full posterior distribution is impractical. Our Gaussian mixture MCMC method allows for the calculation of the full posterior distribution by sampling the facies configurations according to the acceptance/rejection probability. The novelty of the method is the use of an MCMC framework with multimodal distributions for the description of the model properties and the facies along the entire seismic trace. Our method is tested on synthetic seismic data, applied to real seismic data, and validated using a well test.

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Utilizing Markov chain Monte Carlo (MCMC) method for improved glottal inverse filtering

10.21437/interspeech.2012-450 ◽

2012 ◽

Author(s):

Harri Auvinen ◽

Tuomo Raitio ◽

Samuli Siltanen ◽

Paavo Alku

Keyword(s):

Monte Carlo ◽

Markov Chain ◽

Markov Chain Monte Carlo ◽

Inverse Filtering ◽

Mcmc Method

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

Proceedings of the International Astronomical Union ◽

10.1017/s1743921313007060 ◽

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.

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A two-step inversion approach for seismic-reservoir characterization and a comparison with a single-loop Markov-chain Monte Carlo algorithm

Geophysics ◽

10.1190/geo2017-0387.1 ◽

2018 ◽

Vol 83 (3) ◽

pp. R227-R244 ◽

Cited By ~ 12

Author(s):

Mattia Aleardi ◽

Fabio Ciabarri ◽

Timur Gukov

Keyword(s):

Monte Carlo ◽

Markov Chain ◽

Markov Chain Monte Carlo ◽

Reservoir Characterization ◽

Gaussian Mixture ◽

Petrophysical Properties ◽

Mcmc Algorithm ◽

Prior Model ◽

Single Loop ◽

Seismic Reservoir

We have evaluated a two-step Bayesian algorithm for seismic-reservoir characterization, which, thanks to some simplifying assumptions, is computationally very efficient. The applicability and reliability of this method are assessed by comparison with a more sophisticated and computer-intensive Markov-chain Monte Carlo (MCMC) algorithm, which in a single loop directly estimates petrophysical properties and lithofluid facies from prestack data. The two-step method first combines a linear rock-physics model (RPM) with the analytical solution of a linearized amplitude versus angle (AVA) inversion, to directly estimate the petrophysical properties, and related uncertainties, from prestack data under the assumptions of a Gaussian prior model and weak elastic contrasts at the reflecting interface. In particular, we use an empirical, linear RPM, properly calibrated for the investigated area, to reparameterize the linear time-continuous P-wave reflectivity equation in terms of petrophysical contrasts instead of elastic constants. In the second step, a downward 1D Markov-chain prior model is used to infer the lithofluid classes from the outcomes of the first step. The single-loop (SL) MCMC algorithm uses a convolutional forward modeling based on the exact Zoeppritz equations, and it adopts a nonlinear RPM. Moreover, it assumes a more realistic Gaussian mixture distribution for the petrophysical properties. Both approaches are applied on an onshore 3D seismic data set for the characterization of a gas-bearing, clastic reservoir. Notwithstanding the differences in the forward-model parameterization, in the considered RPM, and in the assumed a priori probability density functions, the two methods yield maximum a posteriori solutions that are consistent with well-log data, although the Gaussian mixture assumption adopted by the SL method slightly improves the description of the multimodal behavior of the petrophysical parameters. However, in the considered reservoir, the main difference between the two approaches remains the very different computational times, the SL method being much more computationally intensive than the two-step approach.

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

E-Jurnal Matematika ◽

10.24843/mtk.2015.v04.i03.p099 ◽

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.

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

SPE Journal ◽

10.2118/194488-pa ◽

2019 ◽

Vol 24 (04) ◽

pp. 1468-1489 ◽

Cited By ~ 2

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.

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On the Markov Chain Monte Carlo (MCMC) method

Sadhana ◽

10.1007/bf02719775 ◽

2006 ◽

Vol 31 (2) ◽

pp. 81-104 ◽

Cited By ~ 7

Author(s):

Rajeeva L. Karandikar

Keyword(s):

Monte Carlo ◽

Markov Chain ◽

Markov Chain Monte Carlo ◽

Mcmc Method

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

Geochronometria ◽

10.1515/geochr-2015-0114 ◽

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.

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