• Title/Summary/Keyword: Bayesian information

Search Result 1,221, Processing Time 0.034 seconds

Bayesian Test for the Difference of Exponential Guarantee Time Parameters

  • Kang, Sang-Gil;Kim, Dal-Ho;Lee, Woo-Dong
    • 한국데이터정보과학회:학술대회논문집
    • /
    • 2004.04a
    • /
    • pp.15-23
    • /
    • 2004
  • When X and Y have independent two parameter exponential distributions, we develop a Bayesian testing procedures for the equality of two location parameters. Under the noninformative prior, we propose a Bayesian test procedures for the equality of two location parameters using fractional Bayes factor and intrinsic Bayes factor. Simulation study and some real data examples are provided.

  • PDF

Bayesian estimation for finite population proportion under selection bias via surrogate samples

  • Choi, Seong Mi;Kim, Dal Ho
    • Journal of the Korean Data and Information Science Society
    • /
    • v.24 no.6
    • /
    • pp.1543-1550
    • /
    • 2013
  • In this paper, we study Bayesian estimation for the finite population proportion in binary data under selection bias. We use a Bayesian nonignorable selection model to accommodate the selection mechanism. We compare four possible estimators of the finite population proportions based on data analysis as well as Monte Carlo simulation. It turns out that nonignorable selection model might be useful for weekly biased samples.

A Comparative Study Of Maximum Likelihood Method With Bayesian Approach In Statistical Parameter Estimation Of Static Systems (정적계통의 통계적 퍼래미터 추정에 있어 최우도법과 Bayes식방법과의 비교연구)

  • 한만춘;최경삼
    • 전기의세계
    • /
    • v.22 no.2
    • /
    • pp.51-56
    • /
    • 1973
  • The comparative study of maximum likelihood estimation with Bayesian approach was made by statistical & computational methods in center of a priori information of static systems and the effect of a priori information on the accuracy of the estimatiion was also analyzed. Through the numerical computations of some examples by digital computer, we concluded that maximum likelihood method is better than Bayesian estimation except for almost certain a priori informations. The study may therefore contribute in identification problems of dynamical systems connected with a priori informations.

  • PDF

Bayesian Prediction Inference for Censored Pareto Model

  • Ko, Jeong-Hwan;Kim, Young-Hoon
    • Journal of the Korean Data and Information Science Society
    • /
    • v.10 no.1
    • /
    • pp.147-154
    • /
    • 1999
  • Using a noninformative prior and an inverted gamma prior, the Bayesian predictive density and the prediction intervals for a future observation or the p - th order statistic of n' future observations from the censord Pareto model have been obtained. In additions, numerical examples are given in order to illustrate the proposed predictive procedure.

  • PDF

A Note on A Bayesian Approach to the Choice of Wavelet Basis Functions at Each Resolution Level

  • Park, Chun-Gun
    • Journal of the Korean Data and Information Science Society
    • /
    • v.19 no.4
    • /
    • pp.1465-1476
    • /
    • 2008
  • In recent years wavelet methods have been focused on block shrinkage or thresholding approaches to accounting for the sparseness of the wavelet representation for an unknown function. The block shrinkage or thresholding methods have been developed in both of classical methods and Bayesian methods. In this paper, we propose a Bayesian approach to selecting wavelet basis functions at each resolution level without MCMC procedure. Simulation study and an application are shown.

  • PDF

Semiparametric Bayesian estimation under functional measurement error model

  • Hwang, Jin-Seub;Kim, Dal-Ho
    • Journal of the Korean Data and Information Science Society
    • /
    • v.21 no.2
    • /
    • pp.379-385
    • /
    • 2010
  • This paper considers Bayesian approach to modeling a flexible regression function under functional measurement error model. The regression function is modeled based on semiparametric regression with penalized splines. Model fitting and parameter estimation are carried out in a hierarchical Bayesian framework using Markov chain Monte Carlo methodology. Their performances are compared with those of the estimators under functional measurement error model without semiparametric component.

Nonparametric Bayesian Estimation for the Exponential Lifetime Data under the Type II Censoring

  • Lee, Woo-Dong;Kim, Dal-Ho;Kang, Sang-Gil
    • Communications for Statistical Applications and Methods
    • /
    • v.8 no.2
    • /
    • pp.417-426
    • /
    • 2001
  • This paper addresses the nonparametric Bayesian estimation for the exponential populations under type II censoring. The Dirichlet process prior is used to provide nonparametric Bayesian estimates of parameters of exponential populations. In the past, there have been computational difficulties with nonparametric Bayesian problems. This paper solves these difficulties by a Gibbs sampler algorithm. This procedure is applied to a real example and is compared with a classical estimator.

  • PDF

Bayesian Network Model for Human Fatigue Recognition (피로 인식을 위한 베이지안 네트워크 모델)

  • Lee Young-sik;Park Ho-sik;Bae Cheol-soo
    • The Journal of Korean Institute of Communications and Information Sciences
    • /
    • v.30 no.9C
    • /
    • pp.887-898
    • /
    • 2005
  • In this paper, we introduce a probabilistic model based on Bayesian networks BNs) for recognizing human fatigue. First of all, we measured face feature information such as eyelid movement, gaze, head movement, and facial expression by IR illumination. But, an individual face feature information does not provide enough information to determine human fatigue. Therefore in this paper, a Bayesian network model was constructed to fuse as many as possible fatigue cause parameters and face feature information for probabilistic inferring human fatigue. The MSBNX simulation result ending a 0.95 BN fatigue index threshold. As a result of the experiment, when comparisons are inferred BN fatigue index and the TOVA response time, there is a mutual correlation and from this information we can conclude that this method is very effective at recognizing a human fatigue.

Bayesian analysis of insurance risk model with parameter uncertainty (베이지안 접근법과 모수불확실성을 반영한 보험위험 측정 모형)

  • Cho, Jaerin;Ji, Hyesu;Lee, Hangsuck
    • Journal of the Korean Data and Information Science Society
    • /
    • v.27 no.1
    • /
    • pp.9-18
    • /
    • 2016
  • In the Heckman-Meyers model, which is frequently referred by IAA, Swiss Solvency Test, EU Solvency II, the assumption of parameter distribution is key factor. While in theory Bayesian analysis somewhat reflects parameter uncertainty using prior distribution, it is often the case where both Heckman-Meyers and Bayesian are necessary to better manage the parameter uncertainty. Therefore, this paper proposes the use of Bayesian H-M CRM, a combination of Heckman-Meyers model and Bayesian, and analyzes its efficiency.

Calculating the Importance of Attributes in Naive Bayesian Classification Learning (나이브 베이시안 분류학습에서 속성의 중요도 계산방법)

  • Lee, Chang-Hwan
    • Journal of the Institute of Electronics Engineers of Korea CI
    • /
    • v.48 no.5
    • /
    • pp.83-87
    • /
    • 2011
  • Naive Bayesian learning has been widely used in machine learning. However, in traditional naive Bayesian learning, we make two assumptions: (1) each attribute is independent of each other (2) each attribute has same importance in terms of learning. However, in reality, not all attributes are the same with respect to their importance. In this paper, we propose a new paradigm of calculating the importance of attributes for naive Bayesian learning. The performance of the proposed methods has been compared with those of other methods including SBC and general naive Bayesian. The proposed method shows better performance in most cases.