• Korean Journal of Logic
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• v.7 no.2
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• pp.121-146
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• 2004

본 논문은 베이즈주의가 확률론을 이용해서 제거적 귀납을 정교하게 발전시키고 있다고 주장한다. 이를 위해 본 논문은 두 가지 작업을 진행한다. 하나는 제거적 귀납이 무엇인가 하는것이고 다른 하나는 제거적 귀납이 베이즈주의에 기여하는 바가 무엇인가 하는 것이다. 먼저 본 논문은 제거적 귀납이 참인 가설을 포함하는 가능한 가설들의 총체로부터 경쟁가설들을 연역적 또는 귀납적으로 제거하고 남는 가설을 선택하는 추론형식임을 밝히고, 이 때 베이즈주의는 제거적 귀납을 정교하게 발전시킨 모습이기 때문에 제거적 귀납으로부터 기술적으로 도움 받을 측면은 없다고 주장한다. 그 대신 본 논문은 베이즈주의가 과학방법론으로 발전되는 데에서 직면하는 여러 가지 문제점을 해결하는 방법에 대해 제거적 귀납으로부터 조언을 얻을 수 있다고 주장한다. 이와 같은 논의를 통해 본 논문은 베이즈주의와 제거적 귀납주의의 결합은 유용한 과학방법론을 만들 수 있을 것으로 전망한다.

• 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.

• Journal of Digital Convergence
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• v.10 no.2
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• pp.293-297
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• 2012

In order to provide with personalized recommendation service in context-awareness environment, the collected context data should be analyzed fast and the objective of user should be able to inferred effectively. But, the context collected from the mobile devices is not suitable for applying the existing inference algorithms as they are due to the omission or uncertainty of information and the efficient algorithms are required for mobile environment. In this paper, the behavior pattern was classified using naive bayes classification for minimize the loss caused by the omission or error of information. And pattern matching was used to effectively learn of the users inclination and infer the behavior purpose. The accuracy of the suggested inference model was evaluated by applying to the application recommendation service in the smart phones.

• Journal of the Korea Society for Simulation
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• v.20 no.4
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• pp.139-147
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• 2011

There is a growing tendency toward a vehicle demand and a utilization of traffic information systems. Due to various kinds of traffic information systems and increasing of communication data, the traffic information service requires a very high IT infrastructure. A cloud computing environment is an essential approach for reducing a IT infrastructure cost. And the traffic information service needs a provisioning scheduling method for managing a resource. So we propose a provisioning scheduling with conditional probability inference (PSCPI) for the traffic information service on cloud environment. PSCPI uses a naive bayse inference technique based on a status of a virtual machine. And PSCPI allocates a job to the virtual machines on the basis of an availability of each virtual machine. Naive bayse based PSCPI provides a high throughput and an high availability of virtual machines for real-time traffic information services.

• Proceedings of the Korean Society of Computer Information Conference
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• 2016.07a
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• pp.95-96
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• 2016

본 논문에서는 디리클레 분포와 베이즈 추론 모델을 활용하여 전자우편을 분류하고 정리하는 방법을 제안한다. 과거 원치 않는 광고성 이메일인 스팸 탐지에서 시작한 전자우편 분류는 지속적인 송수신 량의 증가와 내용의 다양화로 인해 광고성과 정보성의 판단 기준이 모호해진 상태이다. 스팸 탐지와 같은 이분법적 분류 방식이 아닌 내용의 주제 별로 자동 분류할 수 있는 방법이 필요하다. 본 논문에서 다루는 제안 기법은 전자우편의 내용에서 다뤄질 수 있는 주제의 종류를 예측하기 위한 방법을 제공한다. 발신하거나 수신된 전자우편이 속한 주제를 자동으로 정할 수 있다. 본 제안 기법의 활용을 통해 전자우편의 분류만이 아닌 업무 및 시장 동향 분석과 정보보안 분야에서는 악성코드 분류에 사용될 수 있을 것으로 기대된다.

• Journal for History of Mathematics
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• v.37 no.1
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• pp.1-17
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• 2024

In this study, we identified the intellectual triggers for Bayesian inference and what key ideas contributed to its occurrence and discussed the practical implications of introducing Bayesian inference into the school mathematics curriculum by reflecting them. The results of the study show that the need for statistical inference about the parameter itself served as a trigger for the occurrence of Bayesian inference, and the most important idea for the occurrence of that inference was to regard the parameter itself as a probability variable rather than any fixed value. On the other hand, these research results suggest that the meaning of introducing Bayesian inference into the secondary mathematics curriculum is 'statistics education that expands the scope of uncertainty'.

• Journal of Digital Contents Society
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• v.18 no.3
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• pp.535-542
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• 2017

The ubiquitous computing made it happen to easily take cognizance of context, which includes user's location, status, behavior patterns and surrounding places. And it allows providing the catered service, designed to improve the quality and the interaction between the provider and its customers. The personalized recommendation service needs to obtain logical reasoning to interpret the context information based on user's interests. We researched a model that connects to the practical value to users for their daily life; information about restaurants, based on several mobile contexts that conveys the weather, time, day and location information. We also have made various approaches including the accurate rating data review, the equation of Naïve Bayes to infer user's behavior-patterns, and the recommendable places pre-selected by preference predictive algorithm. This paper joins a vibrant conversation to demonstrate the excellence of this approach that may prevail other previous rating method systems.

• The Korean Journal of Applied Statistics
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• v.35 no.3
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• pp.445-455
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• 2022

In this paper, we introduce existing Bayesian methods for high-dimensional sparse regression models and compare their performance in various simulation scenarios. Especially, we focus on the variational Bayes approach proposed by Ray and Szabó (2021), which enables scalable and accurate Bayesian inference. Based on simulated data sets from sparse high-dimensional linear regression models, we compare the variational Bayes approach with other Bayesian and frequentist methods. To check the practical performance of the variational Bayes in logistic regression models, a real data analysis is conducted using leukemia data set.

• Journal for History of Mathematics
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• v.13 no.1
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• pp.99-112
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• 2000

In this paper we discuss the teaching methods about statistical inferences. Bayesian methods have the attractive feature that statistical conclusions can be stated using the language of subjective probability. Simple methods of teaching Bayes' rule described, and these methods are illustrated for inference and prediction problems for one proportions. Also, we discuss the advantages and disadvantages of traditional and Bayesian approachs in teaching inference.

• Journal of the Korean Data and Information Science Society
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• v.27 no.5
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• pp.1133-1146
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• 2016

Counts or averages over arbitrary regions are often analyzed using conditionally autoregressive (CAR) models. The spatial neighborhoods within CAR model are generally formed using only the inter-distance or boundaries between the sub-regions. Kyung and Ghosh (2009) proposed a new class of models to accommodate spatial variations that may depend on directions, using different weights given to neighbors in different directions. The proposed model, directional conditionally autoregressive (DCAR) model, generalized the usual CAR model by accounting for spatial anisotropy. Bayesian inference method is discussed based on efficient Markov chain Monte Carlo (MCMC) sampling of the posterior distributions of the parameters. The method is illustrated using a data set of median property prices across Greater Glasgow, Scotland, in 2008.