• Title/Summary/Keyword: 사후확률

Search Result 242, Processing Time 0.025 seconds

베이즈 법칙을 활용한 미니탭 매크로 - 한글 미니탭 Release 14를 이용 -

  • Baek, Ho-Yu;Lee, Jeong-Mi
    • Proceedings of the Korean Statistical Society Conference
    • /
    • 2005.11a
    • /
    • pp.133-139
    • /
    • 2005
  • 베이즈 법칙에서는 사전확률과 우도가 주어지고 어떤 실험결과가 일어났을 때 사후확률을 계산한다. 이러한 사후확률의 계산 문제를 미니탭 매크로를 이용하여 쉽게 계산할 수 있다. 또한 일련의 독립적이고 연속적인 실험결과에 따르는 사후확률도 편리하게 계산할 수 있다. 최근에는 미니탭 한글 Release 14가 출시되어 한글로 결과를 나타낼 수 있도록 매크로를 작성할 수 있다.

  • PDF

Excel macro for applying Bayes' rule (베이즈 법칙의 활용을 위한 엑셀 매크로)

  • Kim, Jae-Hyun;Baek, Hoh-Yoo
    • Journal of the Korean Data and Information Science Society
    • /
    • v.22 no.6
    • /
    • pp.1183-1197
    • /
    • 2011
  • The prior distribution is the probability distribution we have before observing data. Using Bayes' rule, we can compute the posterior distribution, the new probability distribution, after observing data. Computing the posterior distribution is much easier than before by using Excel VBA macro. In addition, we can conveniently compute the successive updating posterior distributions after observing the independent and sequential outcomes. In this paper we compose some Excel VBA macros for applying Bayes' rule and give some examples.

사후 확률.확률 밀도 함수의 추정과 Probabilistic neural network을 이요한 모음 인식에 의한 평가

  • 허강인;이광석;김명기
    • The Journal of the Acoustical Society of Korea
    • /
    • v.12 no.6
    • /
    • pp.21-27
    • /
    • 1993
  • 계층형 신경망은 패턴 분류를 위해 사용되어 왔다. 이것은 주어진 교사패턴들의 학습으로 원하는 입력-출력 간의 매핑을 할 수 있기 때문이다. 신경망은 타겟ㅌ트 패턴이 입력 패턴의 카테고리에 일치할 때 타겟트 패턴을 학습하므로서 사후 확률을 근사화할 수 있다. 그리고 입력 공간을 부분 공간으로 나누어 학습 데이터들의 비율로서 만든 타겟트 벡터들로 학습한 신경망은 확률밀도 함수를 나타낼 수 있다. 본 연구에서는 역전파 학습법을 이용한 계층형 NN 과 코드북으로서 사후 확률과 확률밀도함수의 측정방법을 제안하였다. VQ 로 추정한 사후확률고 확률밀도함수를 이용하여 학습이 필요없는 RBF network 의 일종인 PNN으로 모음 인식을 수행 하였다. 인식 실험에서 PNN 의 결과는 역전파 학습법을 이용항 3층 신경망과 VQ 의 평균 인식율과 비교되었다. VQ-PNN의 인식율이 다른 것보다 우수하게 나타났다.

  • PDF

Bayesian Variable Selection in Linear Regression Models with Inequality Constraints on the Coefficients (제한조건이 있는 선형회귀 모형에서의 베이지안 변수선택)

  • 오만숙
    • The Korean Journal of Applied Statistics
    • /
    • v.15 no.1
    • /
    • pp.73-84
    • /
    • 2002
  • Linear regression models with inequality constraints on the coefficients are frequently used in economic models due to sign or order constraints on the coefficients. In this paper, we propose a Bayesian approach to selecting significant explanatory variables in linear regression models with inequality constraints on the coefficients. Bayesian variable selection requires computation of posterior probability of each candidate model. We propose a method which computes all the necessary posterior model probabilities simultaneously. In specific, we obtain posterior samples form the most general model via Gibbs sampling algorithm (Gelfand and Smith, 1990) and compute the posterior probabilities by using the samples. A real example is given to illustrate the method.

Clustering Algorithm for Data Mining using Posterior Probability-based Information Entropy (데이터마이닝을 위한 사후확률 정보엔트로피 기반 군집화알고리즘)

  • Park, In-Kyoo
    • Journal of Digital Convergence
    • /
    • v.12 no.12
    • /
    • pp.293-301
    • /
    • 2014
  • In this paper, we propose a new measure based on the confidence of Bayesian posterior probability so as to reduce unimportant information in the clustering process. Because the performance of clustering is up to selecting the important degree of attributes within the databases, the concept of information entropy is added to posterior probability for attributes discernibility. Hence, The same value of attributes in the confidence of the proposed measure is considerably much less due to the natural logarithm. Therefore posterior probability-based clustering algorithm selects the minimum of attribute reducts and improves the efficiency of clustering. Analysis of the validation of the proposed algorithms compared with others shows their discernibility as well as ability of clustering to handle uncertainty with ACME categorical data.

Effects of Numerical Formats and Frequency ranges on Judgment of Risk and Inference in the Bayesian InferenceTask (숫자양식과 빈도범위가 베이스 추론 과제에서 위험판단과 추론에 미치는 영향)

  • Lee, Hyun-Ju;Lee, Young-Ai
    • Korean Journal of Cognitive Science
    • /
    • v.20 no.3
    • /
    • pp.335-355
    • /
    • 2009
  • We examined risk judgment and the accuracy of inference based on two kinds of probabilities in a Bayesian inference task: the death probability from a disease (base rates) and the probability of having a disease with positive results in the screening test (posterior probabilities). Risk information were presented in either a probability or a frequency format. In Study 1, we found a numerical format effect for both base rate and posterior probability. Participants rated information as riskier and inferred more accurately in the frequency condition than in the probability condition for both base rate and posterior probability. However, there was no frequency range effect, which suggested that the ranges of frequency format did not influence risk ratings. In order to find out how the analytic thought system influences risk ratings, we compared the ratings of a computation condition and those of a no-computation condition and still found the numerical format effect in computation condition. In Study 2, we examined the numerical format effect and frequency range effect in a high and a low probability condition and found the numerical format effect at each probability level. This result suggests that people feel riskier in the frequency format than in the probability format regardless of the base rates and the posterior probability. We also found a frequency range effect only for the low base rate condition. Our results were discussed in terms of the dual process theories.

  • PDF

A research on Bayesian inference model of human emotion (베이지안 이론을 이용한 감성 추론 모델에 관한 연구)

  • Kim, Ji-Hye;Hwang, Min-Cheol;Kim, Jong-Hwa;U, Jin-Cheol;Kim, Chi-Jung;Kim, Yong-U
    • Proceedings of the Korean Society for Emotion and Sensibility Conference
    • /
    • 2009.11a
    • /
    • pp.95-98
    • /
    • 2009
  • 본 연구는 주관 감성에 따른 생리 데이터의 패턴을 분류하고, 임의의 생리 데이터의 패턴을 확인하여 각성-이완, 쾌-불쾌의 감성을 추론하기 위해 베이지안 이론(Bayesian learning)을 기반으로 한 추론 모델을 제안하는 것이 목적이다. 본 연구에서 제안하는 모델은 학습데이터를 분류하여 사전확률을 도출하는 학습 단계와 사후확률로 임의의 생리 데이터의 패턴을 분류하여 감성을 추론하는 추론 단계로 이루어진다. 자율 신경계 생리변수(PPG, GSR, SKT) 각각의 패턴 분류를 위해 1~7로 정규화를 시킨 후 선형 관계를 구하여 분류된 패턴의 사전확률을 구하였다. 다음으로 임의의 사전 확률 분포에 대한 사후 확률 분포의 계산을 위해 베이지안 이론을 적용하였다. 본 연구를 통해 주관적 평가를 실시하지 않고 다중 생리변수 인식을 통해 감성을 추론 할 수 있는 모델을 제안하였다.

  • PDF

A Statistical Model-Based Voice Activity Detection Employing the Conditional MAP Criterion with Spectral Deviation (조건 사후 최대 확률과 음성 스펙트럼 변이 조건을 이용한 통계적 모델 기반의 음성 검출기)

  • Kim, Sang-Kyun;Chang, Joon-Hyuk
    • The Journal of the Acoustical Society of Korea
    • /
    • v.30 no.6
    • /
    • pp.324-329
    • /
    • 2011
  • In this paper, we propose a novel approach to improve the performance of a statistical model-based voice activity detection (VAD) which is based on the conditional maximum a posteriori (CMAP) with deviation. In our approach, the VAD decision rule is expressed as the geometric mean of likelihood ratios (LRs) based on adapted threshold according to the speech presence probability conditioned on both the speech activity decisions and spectral deviation in the pervious frame. Experimental results show that the proposed approach yields better results compared to the CMAP-based VAD using the LR test.

Fast Bayesian Inversion of Geophysical Data (지구물리 자료의 고속 베이지안 역산)

  • Oh, Seok-Hoon;Kwon, Byung-Doo;Nam, Jae-Cheol;Kee, Duk-Kee
    • Journal of the Korean Geophysical Society
    • /
    • v.3 no.3
    • /
    • pp.161-174
    • /
    • 2000
  • Bayesian inversion is a stable approach to infer the subsurface structure with the limited data from geophysical explorations. In geophysical inverse process, due to the finite and discrete characteristics of field data and modeling process, some uncertainties are inherent and therefore probabilistic approach to the geophysical inversion is required. Bayesian framework provides theoretical base for the confidency and uncertainty analysis for the inference. However, most of the Bayesian inversion require the integration process of high dimension, so massive calculations like a Monte Carlo integration is demanded to solve it. This method, though, seemed suitable to apply to the geophysical problems which have the characteristics of highly non-linearity, we are faced to meet the promptness and convenience in field process. In this study, by the Gaussian approximation for the observed data and a priori information, fast Bayesian inversion scheme is developed and applied to the model problem with electric well logging and dipole-dipole resistivity data. Each covariance matrices are induced by geostatistical method and optimization technique resulted in maximum a posteriori information. Especially a priori information is evaluated by the cross-validation technique. And the uncertainty analysis was performed to interpret the resistivity structure by simulation of a posteriori covariance matrix.

  • PDF

Creation of Approximate Rules based on Posterior Probability (사후확률에 기반한 근사 규칙의 생성)

  • Park, In-Kyu;Choi, Gyoo-Seok
    • The Journal of the Institute of Internet, Broadcasting and Communication
    • /
    • v.15 no.5
    • /
    • pp.69-74
    • /
    • 2015
  • In this paper the patterns of information system is reduced so that control rules can guarantee fast response of queries in database. Generally an information system includes many kinds of necessary and unnecessary attribute. In particular, inconsistent information system is less likely to acquire the accuracy of response. Hence we are interested in the simple and understandable rules that can represent useful patterns by means of rough entropy and Bayesian posterior probability. We propose an algorithm which can reduce control rules to a minimum without inadequate patterns such that the implication between condition attributes and decision attributes is measured through the framework of rough entropy. Subsequently the validation of the proposed algorithm is showed through test information system of new employees appointment.