Bayesian MCMC methods of portfolio selection analysis

Abstract In this paper, we study uncertainty in estimating extreme floods of the Dongting Lake basin, China. We used three methods, including the Delta, profile likelihood function (PLF), and the Bayesian Markov chain Monte Carlo (MCMC) methods, to calculate confidence intervals of parameters of the generalized extreme value (GEV) distribution and quantiles of extreme floods. The annual maximum flow (AMF) data from four hydrologic stations were selected. Our results show that AMF data from Taoyuan and Xiangtan stations followed the Weibull class distribution, while the data from Shimen and Taojiang stations followed the Fréchet class distribution. The three methods show similar confidence intervals of design floods for short return periods. However, there are large differences between results of the Delta and the other two methods for long return periods. Both PLF and Bayesian MCMC methods have similar confidence intervals to reflect the uncertainty of design floods. However, because the PLF method is quite burdensome in computation, the Bayesian MCMC method is more suitable for practical use.

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Portfolio selection using hierarchical Bayesian analysis and MCMC methods

Journal of Banking & Finance ◽

10.1016/j.jbankfin.2005.04.008 ◽

2006 ◽

Vol 30 (2) ◽

pp. 669-678 ◽

Cited By ~ 19

Author(s):

Alex Greyserman ◽

Douglas H. Jones ◽

William E. Strawderman

Keyword(s):

Bayesian Analysis ◽

Portfolio Selection ◽

Mcmc Methods ◽

Hierarchical Bayesian ◽

Hierarchical Bayesian Analysis

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Occupancy Estimation Based on Indoor CO2 Concentration: Comparison of Neural Network and Bayesian Methods

International Journal of Air-Conditioning and Refrigeration ◽

10.1142/s2010132517500213 ◽

2017 ◽

Vol 25 (03) ◽

pp. 1750021 ◽

Cited By ~ 4

Author(s):

Haolia Rahman ◽

Hwataik Han

Keyword(s):

Neural Network ◽

Input Parameter ◽

Co2 Concentration ◽

Ventilation Rate ◽

Estimation Accuracy ◽

Mcmc Methods ◽

Bayesian Mcmc ◽

The Neural Network ◽

Occupancy Patterns ◽

Input Dataset

The number of occupants in a space can significantly affect ventilation control. Using neural network and Bayesian Markov chain Monte Carlo (MCMC) methods, this study estimates the number of occupants based on CO2 concentration in a room. The abilities of both methods to recognize the input-parameter characteristics are compared under certain circumstances, and the parameters are optimized to improve the estimation accuracy. The neural network trains an input dataset of CO2 concentrations, ventilation rates, and occupancy patterns with tapped delay lines. Meanwhile, the Bayesian MCMC calculates the given CO2 data by a mathematical model based on a statistical approach. The present space model is a single-office room in which the CO2 concentration is determined through several simulation schemes and experiments. The estimation accuracy of the neural network depends on the complexity of the input parameters (i.e., CO2 concentration and ventilation rate), whereas the Bayesian MCMC is influenced by uncertainty in the CO2 concentration. Both methods produce acceptable estimates under certain treatments.

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