• Title/Summary/Keyword: posterior probability

Search Result 224, Processing Time 0.021 seconds

A Bayesian Approach to Finite Population Sampling Using the Concept of Pivotal Quantity

  • Hwang, Hyungtae
    • Communications for Statistical Applications and Methods
    • /
    • v.10 no.3
    • /
    • pp.647-654
    • /
    • 2003
  • Bayesian probability models for finite populations are considered assuming so-called the super-population. We find the posterior distribution of population mean by a new approach, using the concept of pivotal quantity for the small sample case. A large sample theory is also treated throught the concept of asymptotically pivotal quantity.

A Study on the Adjustment of Posterior Probability for Oversampling when the Target is Rare (목표 범주가 희귀한 자료의 과대표본추출에 대한 연구)

  • Kim, U.N.;Lee, S.K.;Choi, J.H.
    • The Korean Journal of Applied Statistics
    • /
    • v.24 no.3
    • /
    • pp.477-484
    • /
    • 2011
  • When an event of target variable is rare, a widespread strategy is to build a model on the sample that disproportionally over-represents the events, that is over-sampled. Using the data over-sampled from the original data set, the predicted values would be biased; however, it can be easily corrected to represent the population. In this study, we investigate into the relationship between the proportion of rare event on a data-mart and the model performance using real world data of a Korean credit card company. Also, we use the methods for adjusting of posterior probability for over-sampled data of the offset method and the weighted method. Finally, we compare the performance of the methods using real data sets.

A Study on Combined DoA Estimation Algorithm using LCMV and Maximum Posterior on Uniform Linear Array Antenna (균일 선형 배열 안테나에서 선형구속최소분산 방법과 사후 추정 확률을 결합한 도래 방향 추정 알고리즘 연구)

  • Lee, Kwan-Hyeong;Park, Sung-Kon;Jeong, Youn-Seo
    • The Journal of Korea Institute of Information, Electronics, and Communication Technology
    • /
    • v.9 no.3
    • /
    • pp.291-297
    • /
    • 2016
  • In this paper, we are comparative analysis of exit algorithm and proposal algorithm for desired target direction of arrival estimation in correlation signal system. Proposed algorithm in this paper is to decrease target direction of arrival an estimation error probability using bayesian, maximum posterior, and MUSIC algorithm in order to decrease direction of arrival error probability as optimize and use linear constrained minimum variance to update weight value. Through simulation, we were comparative analysis proposed algorithm and exit MUSIC algorithm. In case SNR is 10dB and antenna element arrays are 9 and 12, We show the superior performance of the proposed method relative to the class method to decrease of signal estimation error probability about 11% and 13%, respectively.

Improvement of Self Organizing Maps using Gap Statistic and Probability Distribution

  • Jun, Sung-Hae
    • International Journal of Fuzzy Logic and Intelligent Systems
    • /
    • v.8 no.2
    • /
    • pp.116-120
    • /
    • 2008
  • Clustering is a method for unsupervised learning. General clustering tools have been depended on statistical methods and machine learning algorithms. One of the popular clustering algorithms based on machine learning is the self organizing map(SOM). SOM is a neural networks model for clustering. SOM and extended SOM have been used in diverse classification and clustering fields such as data mining. But, SOM has had a problem determining optimal number of clusters. In this paper, we propose an improvement of SOM using gap statistic and probability distribution. The gap statistic was introduced to estimate the number of clusters in a dataset. We use gap statistic for settling the problem of SOM. Also, in our research, weights of feature nodes are updated by probability distribution. After complete updating according to prior and posterior distributions, the weights of SOM have probability distributions for optima clustering. To verify improved performance of our work, we make experiments compared with other learning algorithms using simulation data sets.

Development of Bayes' rule education tool with Excel Macro (엑셀 매크로기능을 이용한 베이즈 정리 교육도구 개발)

  • Choi, Hyun-Seok;Ha, Jeong-Cheol
    • Journal of the Korean Data and Information Science Society
    • /
    • v.23 no.5
    • /
    • pp.905-912
    • /
    • 2012
  • We are dealing with the Bayes' rule education tool with Excel Macro and its usage example. When an event occurs, we are interested in whether it does under certain conditions or not. In this case, we use the Bayes' rule to calculate the probability. Bayes' rule is very useful in making decision based on newly obtained statistical information. We introduce an efficient self-teaching educational tool developed to help the learners understand the Bayes' rule through intermediate steps and descriptions. The concept and examples of intermediate steps such as conditional probability, multiplication rule, law of total probability, prior probability and posterior probability could be acquired through step-by-step learning. All the processes leading to result are given with diagrams and detailed descriptions. By just clicking the execution button, users could get the results in one screen.

Predicting typhoons in Korea (국내 태풍 예측)

  • Yang, Heejoong
    • Journal of the Korea Safety Management & Science
    • /
    • v.17 no.1
    • /
    • pp.169-177
    • /
    • 2015
  • We develop a model to predict typhoons in Korea. We collect data for typhoons and classify those depending on the severity level. Following a Bayesian approach, we develop a model that explains the relationship between different levels of typhoons. Through the analysis of the model, we can predict the rate of typhoons, the probability of approaching Korean peninsular, and the probability of striking Korean peninsular. We show that the uncertainty for the occurrence of various types of typhoons reduces dramatically by adaptively updating model parameters as we acquire data.

A Study on Effective Identification Method for Influential Main Effects and Interactions in the 2-level Factorial Designs (2-수준 요인실험에서 주효과 및 교호작용에 대한 효율적인 분석방법 연구)

  • Kim, Sang-Ik
    • Journal of Korean Society for Quality Management
    • /
    • v.34 no.1
    • /
    • pp.27-33
    • /
    • 2006
  • In this paper, an effective method for identifying influential main effects and interactions in the 2-level factorial designs is suggested by exploiting the resolution V designs developed by Kim(1992). For analysis of such designs, we employ the Bayesian approach for easy and clear identification of influential effects in the half normal probability plot.

Noninformative priors for the ratio of parameters of two Maxwell distributions

  • Kang, Sang Gil;Kim, Dal Ho;Lee, Woo Dong
    • Journal of the Korean Data and Information Science Society
    • /
    • v.24 no.3
    • /
    • pp.643-650
    • /
    • 2013
  • We develop noninformative priors for a ratio of parameters of two Maxwell distributions which is used to check the equality of two Maxwell distributions. Specially, we focus on developing probability matching priors and Je reys' prior for objectiv Bayesian inferences. The probability matching priors, under which the probability of the Bayesian credible interval matches the frequentist probability asymptotically, are developed. The posterior propriety under the developed priors will be shown. Some simulations are performed for identifying the usefulness of proposed priors in objective Bayesian inference.

Development of Matching Priors for P(X < Y) in Exprnential dlstributions

  • Lee, Gunhee
    • Journal of the Korean Statistical Society
    • /
    • v.27 no.4
    • /
    • pp.421-433
    • /
    • 1998
  • In this paper, matching priors for P(X < Y) are investigated when both distributions are exponential distributions. Two recent approaches for finding noninformative priors are introduced. The first one is the verger and Bernardo's forward and backward reference priors that maximizes the expected Kullback-Liebler Divergence between posterior and prior density. The second one is the matching prior identified by matching the one sided posterior credible interval with the frequentist's desired confidence level. The general forms of the second- order matching prior are presented so that the one sided posterior credible intervals agree with the frequentist's desired confidence levels up to O(n$^{-1}$ ). The frequentist coverage probabilities of confidence sets based on several noninformative priors are compared for small sample sizes via the Monte-Carlo simulation.

  • PDF

Nonparametric Bayesian Multiple Comparisons for Geometric Populations

  • Ali, M. Masoom;Cho, J.S.;Begum, Munni
    • Journal of the Korean Data and Information Science Society
    • /
    • v.16 no.4
    • /
    • pp.1129-1140
    • /
    • 2005
  • A nonparametric Bayesian method for calculating posterior probabilities of the multiple comparison problem on the parameters of several Geometric populations is presented. Bayesian multiple comparisons under two different prior/ likelihood combinations was studied by Gopalan and Berry(1998) using Dirichlet process priors. In this paper, we followed the same approach to calculate posterior probabilities for various hypotheses in a statistical experiment with a partition on the parameter space induced by equality and inequality relationships on the parameters of several geometric populations. This also leads to a simple method for obtaining pairwise comparisons of probability of successes. Gibbs sampling technique was used to evaluate the posterior probabilities of all possible hypotheses that are analytically intractable. A numerical example is given to illustrate the procedure.

  • PDF