• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2009.11a
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• pp.95-98
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• 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.

• The Journal of the Korea Contents Association
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• v.21 no.11
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• pp.77-86
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• 2021

A method of constructing a war simulation based on Bayesian Inference was proposed as a method of constructing heterogeneous historical war data obtained with a time difference into a single model. A method of applying a linear regression model can be considered as a method of predicting future battles by analyzing historical war results. However it is not appropriate for two heterogeneous types of historical data that reflect changes in the battlefield environment due to different times to be suitable as a single linear regression model and violation of the model's assumptions. To resolve these problems a Bayesian inference method was proposed to obtain a post-distribution by assuming the data from the previous era as a non-informative prior distribution and to infer the final posterior distribution by using it as a prior distribution to analyze the data obtained from the next era. Another advantage of the Bayesian inference method is that the results sampled by the Markov Chain Monte Carlo method can be used to infer posterior distribution or posterior predictive distribution reflecting uncertainty. In this way, it has the advantage of not only being able to utilize a variety of information rather than analyzing it with a classical linear regression model, but also continuing to update the model by reflecting additional data obtained in the future.

• The Korean Journal of Applied Statistics
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• v.34 no.6
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• pp.863-874
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• 2021

In this paper, we propose a post-selection inference procedure for the fused lasso signal approximator (FLSA). The FLSA finds underlying sparse piecewise constant mean structure by applying total variation (TV) semi-norm as a penalty term. However, it is widely known that this convex relaxation can cause asymptotic inconsistency in change points detection. As a result, there can remain false change points even though we try to find the best subset of change points via a tuning procedure. To remove these false change points, we propose a post-selection inference for the FLSA. The proposed procedure applies a permutation test based on CUSUM statistic. Our post-selection inference procedure is an extension of the permutation test of Antoch and Hušková (2001) which deals with single change point problems, to multiple change points detection problems in combination with the FLSA. Numerical study results show that the proposed procedure is better than naïve z-tests and tests based on the limiting distribution of CUSUM statistics.

• Korean Journal of Cognitive Science
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• v.20 no.3
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• pp.335-355
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• 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.

• Journal of the Korean Chemical Society
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• v.67 no.5
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• pp.362-377
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• 2023

The purpose of this study was to investigate the effect of semantic mapping as a science text reading strategy on high school students' inferential understanding. For this purpose, eight science text reading classes were conducted a reading strategy using semantic mapping for 46 students in two science-focused classes in the third grade of a high school. To investigate the effects of semantic mapping reading strategy on students' inferential comprehension, students' pre- and post-reading ability tests results were analyzed. In order to find out the change in inferential comprehension, the level of the inferential comprehension was analyzed using the analysis framework for developed in this study. For the classification of inferential comprehension, the levels of the inferential comprehension were converted into scores. The results of the analysis of changes in students' inferential comprehension showed that semantic mapping reading strategy classes influenced the changes in high school students' inference, especially bridge inference and elaborative inference among sub-elements of inferential comprehension.

• Journal of Oral Medicine and Pain
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• v.30 no.1
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• pp.35-44
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• 2005

Purpose: The aim of this preliminary study was to evaluate the effect of Problem-Based Learning (PBL) curriculum on development of comprehension of basic medical knowledge and quality of semi-structured problem solving including scientific reasoning skill. This scientific reasoning contained five components including: size of simple, design of research cause-effect, construction of risk factor, analysis statistic of data, interpretation of result. Materials and Methods: Seoul National University Dental students (100) participated in this experience during two weeks, 2004. Forty eight multiple-choice questions (MCQ) concerned "Infection Control and Prevention" were asked before and after two sections of Lecture-Based Learning (LBL) and PBL (pretest-posttest control group design). A semi-structured problem in epidemiological research was asked to these students after two sections (posttest-only control group design). Data (mean and SD) were analysed using the t Test for two independent samples (p<.05), comparing PBL versus LBL. Results: Our analyse of scores show no difference between LBL and PBL in the development of comprehension of "Infection Control and Prevention". The quality problem solving (epidemiological research) was significantly different between the two groups (p=.029); specially, two components' scores of reflection on scientific reasoning cause-effect (p=.000) and interpretation of result (p=.001) were significantly better for PBL than for LBL. Conclusion: Theses results indicate that comparing LBL and PBL, PBL curriculum have not been disadvantaged in comprehension of basic knowledge, and have contributed to develop the scientific reasoning in problem solving.

• Communications for Statistical Applications and Methods
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• v.3 no.1
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• pp.169-177
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• 1996

본 논문에서는 한 유명 농구선수의 과거의 연도별 평균득점과 평균 야투율을 기초로 앞으로의 경기에 대한 평균득점과 평균야투율을 추정하기 위해 몬테칼로 베이지안 분석법 중의 하나인 Sampling-Important-Resampling (SIR) 알고리즘을 이용하였다. 즉 과거의 자료로부터 평균득점과 평균야투율에 대한 사전밀도함수를 설정하고 SIR 알고리즘을 이용하여 사후 밀도함수를 구한 후에 이를 기초로 베이지안 추론을 하였다.

• Communications for Statistical Applications and Methods
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• v.4 no.3
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• pp.753-763
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• 1997

컴퓨터의 발전에 따른 MCMC를 비동질적 포아송 과정에 이용하였다. 베이지안 추론에서 조건부 분포를 가지고 사후분포를 결정하는데 있어서의 계산 문제를 고려하였다. 특히 분포가 이중지수, 곰페르츠, 랄리, 감마, 그리고 검벨인 일반 순서통계량 모형에 대하여 깁스 샘플링과 메트로폴리스 알고리즘을 활용한 베이지안 계산과 모형선택을 제시하였다.

• Journal of Intelligence and Information Systems
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• v.7 no.1
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• pp.47-61
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• 2001

Lazy loaming methods including CBR have relative advantages in comparison with eager loaming methods such as artificial neural networks and decision trees. However, they are very sensitive to irrelevant features. In other words, when there are irrelevant features, larry learning methods have difficulty in comparing cases. Therefore, their performance can be degraded significantly. To overcome this disadvantage, feature weighting methods for lazy loaming methods have been studied. Most of the existing researches, however, were focused on global feature weighting. In this research, we propose a new local feature weighting method, which we shall call CBDFW. CBDFW stores classification performance of randomly generated feature weight vectors. Then, given a new query case, CBDFW retrieves the successful feature weight vectors and designs a feature weight vector fur the query case. In the test on credit evaluation domain, CBDFW showed better classification accuracy when compared to the results of previous researches.

• The Korean Journal of Applied Statistics
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• v.34 no.1
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• pp.9-23
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• 2021

In the following paper we introduce a variational Bayes method that approximates posterior distributions with mean-field method. In particular, we introduce automatic differentiation variation inference (ADVI), which approximates joint posterior distributions using the product of Gaussian distributions after transforming parameters into real coordinate space, and then apply it to pharmacokinetic models that are models for the study of the time course of drug absorption, distribution, metabolism and excretion. We analyze real data sets using ADVI and compare the results with those based on Markov chain Monte Carlo. We implement the algorithms using Stan.