List of Articles Markov&#x0D

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  1 - Comparing of Bayesian Model Selection Based on MCMC Method and Finance Time Series(GARCH Model)
  محمدرضا صالحی راد نفیسه حبیب یفرد
  By using the time series models, we can analysis financial data(in last and futuretime). In financial discussions, because of heteroskedastic observations, we can notuse the classical time series models.We focus on popular practical models for financial time series, GAR More

  By using the time series models, we can analysis financial data(in last and futuretime). In financial discussions, because of heteroskedastic observations, we can notuse the classical time series models.We focus on popular practical models for financial time series, GARCH- typemodels, that were introduced for the first time by Bollerslev(1986). These modelsrepresent a very wide class of heteroskedastic econometric models. Time seriesmodels(GARCH models too), like regression models, have random errors. Theseerrors have specific distributions.Since that, the GARCH models variability is not clear, thus, we use the Bayesianmodel selection methods to estimate the parameters of the model. In this method, byusing the prior distributions on the parameters, we find the posterior distributionwhich has integral. Then, we can inference about the parameters.To explore the role of the posterior distribution, the most powerful technique is touse Markov Chain Monte Carlo (MCMC) computing methods such as the Gibbssampler and the Metropolis Hasting (MH) algorithm. These algorithms enable toestimate the posterior distribution, but, they don’t readily lend themselves to estimateaspects of the model probabilities. The most widely used one is the group of directmethods, such as the harmonic mean estimator, importance sampling and bridgesampling. Chib(1995 and 2001) proposed an indirect method for estimating modellikelihoods from Gibbs sampling output. This idea has recently been extended to theoutput of the MH algorithm.We use a reversible jump MCMC strategy for generating samples from the jointposterior distribution based on the standard MH approach. Manuscript profile
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  2 - Analysis and forecasting of precipitation in the Larestan area by Markov chain.
  بهلول Alijani زین العابدین Jafarpoor حیدر Ghaderi
  In order to analyze the precipitation of the Larestan area, the rain days with 0.1millimeter or more were obtained from the Iranian Meteorological Organization for the1960-2003 period. First the rainy periods with different lengths were identified andtheir monthly and s More

  In order to analyze the precipitation of the Larestan area, the rain days with 0.1millimeter or more were obtained from the Iranian Meteorological Organization for the1960-2003 period. First the rainy periods with different lengths were identified andtheir monthly and seasonal frequencies were calculated. On the monthly basis Januaryhad the highest wet days frequency and winter was the wettest but the spring was thedriest season. The wettest year had 44 rain days while only 11 days were experiencedduring the dry year. The mean daily density of rain was 8.2 mm and the mean timeinterval between successive rainy periods was 6.2 days. On the average the rainyperiod begins each year on 8 of December and ends on 6 of April.The first order Markov chain was applied to the data series to forecast the wetperiods. The model responded well and was able to forecast significantly andprecisely. The model was fitted best for the runs of one to six days proving thehypothesis of the study. Manuscript profile