• Title/Summary/Keyword: Bayesian model

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A Study of Short-term Won/Doller Exchange rate Prediction Model using Hidden Markov Model (은닉마아코프모델을 이용한 단기 원/달러 환율예측 모형 연구)

  • Jeon, Jin-Ho;Kim, Min-Soo
    • The Journal of the Institute of Internet, Broadcasting and Communication
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    • v.12 no.5
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    • pp.229-235
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    • 2012
  • Forex trading participants, due to the intensified economic internationalization exchange risk avoidance measures are needed. In this research, Model suitable for estimation of time-series data, such as stock prices and exchange rates, through the concealment of HMM and estimate the short-term exchange rate forecasting model is applied to the prediction of the future. Estimated by applying the optimal model if the real exchange rate data for a certain period of the future will be able to predict the movement aspect of it. Alleged concealment of HMM. For the estimation of the model to accurately estimate the number of states of the model via Bayesian Information Criterion was confirmed as a model predictive aspect of physical exercise aspect and predict the movement of the two curves were similar.

Genetic parameters for daily milk somatic cell score and relationships with yield traits of primiparous Holstein cattle in Iran

  • Kheirabadi, Khabat;Razmkabir, Mohammad
    • Journal of Animal Science and Technology
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    • v.58 no.10
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    • pp.38.1-38.6
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    • 2016
  • Background: Despite the importance of relationships between somatic cell score (SCS) and currently selected traits (milk, fat and protein yield) of Holstein cows, there was a lack of comprehensive literature for it in Iran. Therefore we tried to examine heritabilities and relationships between these traits using a fixed-regression animal model and Bayesian inference. The data set consisted of 1,078,966 test-day observations from 146,765 primiparous daughters of 1930 sires, with calvings from 2002 to 2013. Results: Marginal posterior means of heritability estimates for SCS ($0.03{\pm}0.002$) were distinctly lower than those for milk ($0.204{\pm}0.006$), fat ($0.096{\pm}0.004$) and protein ($0.147{\pm}0.005$) yields. In the case of phenotypic correlations, the relationships between production and SCS were near zero at the beginning of lactation but become increasingly negative as days in milk increased. Although all environmental correlations between production and SCS were negative ($-0.177{\pm}0.007$, $-0.165{\pm}0.008$ and $-0.152{\pm}0.007$ between SCS and milk, fat, and protein yield, respectively), slightly antagonistic genetic correlations were found; with posterior mean of relationships ranging from $0.01{\pm}0.039$ to $0.11{\pm}0.036$. This genetic opposition was distinctly higher for protein than for fat. Conclusion: Although small, the positive genetic correlations suggest some genetic antagonism between desired increased milk production and reduced SCS (i.e., single-trait selection for increased milk production will also increase SCS).

An Optimization Method of Neural Networks using Adaptive Regulraization, Pruning, and BIC (적응적 정규화, 프루닝 및 BIC를 이용한 신경망 최적화 방법)

  • 이현진;박혜영
    • Journal of Korea Multimedia Society
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    • v.6 no.1
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    • pp.136-147
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    • 2003
  • To achieve an optimal performance for a given problem, we need an integrative process of the parameter optimization via learning and the structure optimization via model selection. In this paper, we propose an efficient optimization method for improving generalization performance by considering the property of each sub-method and by combining them with common theoretical properties. First, weight parameters are optimized by natural gradient teaming with adaptive regularization, which uses a diverse error function. Second, the network structure is optimized by eliminating unnecessary parameters with natural pruning. Through iterating these processes, candidate models are constructed and evaluated based on the Bayesian Information Criterion so that an optimal one is finally selected. Through computational experiments on benchmark problems, we confirm the weight parameter and structure optimization performance of the proposed method.

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Competition Policy and Open Access to Essential Facilities in Natural Gas Market (천연가스시장 경쟁도입과 필수설비 공유의 효과 분석)

  • Heo, Eun Jeong;Cho, Myeonghwan
    • Environmental and Resource Economics Review
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    • v.29 no.1
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    • pp.47-89
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    • 2020
  • We introduce a simple theoretical model to analyze the welfare impact of a competition policy in the natural gas market in South Korea. An incumbent monopolistic firm currently owns essential facilities, but the competition policy mandates that the firm provide open access to any entrant firm, charging an access fee. When no regulation is imposed on the fee pricing, this policy increases social welfare as well as the profit of the incumbent firm. When the pricing is regulated, however, social welfare depends on whether there is information asymmetry between the government and the firm regarding the operating cost of the facilities. If the government has complete information, social welfare can be maximized by choosing the optimal prices. Otherwise, the government has to set the prices based on the information that the firm delivers. We formulate a Bayesian game to analyze this case and identify a set of perfect Bayesian equilibria to compare social welfare.

A Context-aware Messenger for Sharing User Contextual Information (사용자 컨텍스트 공유를 위한 상황인지 메신저)

  • Hong, Jin-Hyuk;Yang, Sung-Ihk;Cho, Sung-Bae
    • Journal of KIISE:Computing Practices and Letters
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    • v.14 no.9
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    • pp.906-910
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    • 2008
  • As the mobile environment becomes widely used, there is a growth on the concern about recognizing and sharing user context. Sharing context makes the interaction between human more plentiful as well as helps to keep a good social relationship. Recently, it has been applied to some messengers or mobile applications with sharing simple contexts, but it is still required to recognize and share more complex and diverse contexts. In this paper, we propose a context-aware messenger that collects various sensory information, recognizes representative user contexts such as emotion, stress, and activity by using dynamic Bayesian networks, and visualizes them. It includes a modular model that is effective to recognize various contexts and displays them in the form of icons. We have verified the proposed method with the scenario evaluation and usability test.

The network reliability based OLSR protocol (네트워크의 신뢰도를 고려한 OLSR 프로토콜)

  • Woo, Hyun-Jae;Lee, Dong-Yul;Lee, Chae-Woo
    • Journal of the Institute of Electronics Engineers of Korea CI
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    • v.45 no.6
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    • pp.68-76
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    • 2008
  • It is difficult to maintain mutes in the mobile ad hoc network (MANET) due to the high probability of disconnected routes break by frequent change of topology. The links can have the different reliability about data transmission due to these characteristics. Hence a measure which can evaluate this reliability and a algorithm which reflects this are required. In this paper, we propose routing algorithm based on reliability about transmission. First the bayesian inference which infers the hypothesis by past information is considered to obtain the link's transmission reliability. The other is that the link-based reliability estimation model which considers each link's reliability additionally is proposed while the standard uses only Dijkstra's shortest path algorithm. the simulation results using NS-2 show that the performance of proposed algorithm is superior to the standard OLSR in terms of throughput and stability.

Comparison of GEE Estimators Using Imputation Methods (대체방법별 GEE추정량 비교)

  • 김동욱;노영화
    • The Korean Journal of Applied Statistics
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    • v.16 no.2
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    • pp.407-426
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    • 2003
  • We consider the missing covariates problem in generalized estimating equations(GEE) model. If the covariate is partially missing, GEE can not be calculated. In this paper, we study the performance of 7 imputation methods to handle missing covariates in GEE models, and the properties of GEE estimators are investigated after missing covariates are imputed for ordinal data of repeated measurements. The 7 imputation methods include i) Naive Deletion ii) Sample Average Imputation iii) Row Average Imputation iv) Cross-wave Regression Imputation v) Carry-over Imputation vi) Bayesian Bootstrap vii) Approximate Bayesian Bootstrap. A Monte-Carlo simulation is used to compare the performance of these methods. For the missing mechanism generating the missing data, we assume ignorable nonresponse. Furthermore, we generate missing covariates with or without considering wave nonresp onse patterns.

Template Fusion for Fingerprint Recognition (지문 등록을 위한 템플릿 융합 알고리즘)

  • 류춘우;문지현;김학일
    • Journal of the Institute of Electronics Engineers of Korea SP
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    • v.41 no.2
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    • pp.51-64
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    • 2004
  • This paper proposes an algerian of generating a tuner-template from multiple fingerprint impressions using a data fusion technique for fingerprint enrollment. The super-template is considered as a single fingerprint template which contains most likely true minutiae based on multiple fingerprint images. The proposed algorithm creates the super template by utilizing a recursive Bayesian estimation method (RBEM), which assumes a sequential fingerprint input model and estimates the credibility of the minutiae in previous input templates froma current input template. Consequently. the RBEM assigns a higher credibility to commonly detectable minutiae from several input templates and a lower credibility to rarely found minutiae from other input templates. Likewise, the RBEM is able to estimate a credibility of the minutia type (ridge ending or bifurcation). Preliminary experiments demonstrate that, as the number of fingerfrint images increases, the performance of recognition can be improved while maintaining the processing time and the size of memory storage for tile super-template almost constant.

Implementation of a Face Authentication Embedded System Using High-dimensional Local Binary Pattern Descriptor and Joint Bayesian Algorithm (고차원 국부이진패턴과 결합베이시안 알고리즘을 이용한 얼굴인증 임베디드 시스템 구현)

  • Kim, Dongju;Lee, Seungik;Kang, Seog Geun
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.21 no.9
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    • pp.1674-1680
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    • 2017
  • In this paper, an embedded system for face authentication, which exploits high-dimensional local binary pattern (LBP) descriptor and joint Bayesian algorithm, is proposed. We also present a feasible embedded system for the proposed algorithm implemented with a Raspberry Pi 3 model B. Computer simulation for performance evaluation of the presented face authentication algorithm is carried out using a face database of 500 persons. The face data of a person consist of 2 images, one for training and the other for test. As performance measures, we exploit score distribution and face authentication time with respect to the dimensions of principal component analysis (PCA). As a result, it is confirmed that an embedded system having a good face authentication performance can be implemented with a relatively low cost under an optimized embedded environment.

Assessment of Drought Vulnerability Using Bayesian Network Model (베이지안 네트워크 모델을 활용한 가뭄 취약성 평가)

  • Kim, Ji Eun;Shin, Ji Yae;Chung, Gunhui;Kim, Tae-Woong
    • Proceedings of the Korea Water Resources Association Conference
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    • 2018.05a
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    • pp.126-126
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    • 2018
  • 최근 우리나라는 기후변화로 인한 이상기후 현상 중 가뭄에 대한 발생빈도가 증가하고 있다. 가뭄은 다른 자연재해에 비해 지속기간이 길고 규모가 광범위하여, 사회 경제적인 피해가 크게 발생한다. 이러한 가뭄에 대비하기 위해서는 지역적으로 적합한 가뭄 대책을 수립해야 하며, 이를 위해서는 가뭄 위험도 평가가 선행되어야 한다. 지역적 가뭄 위험도를 평가하기 위해서는 기상학적 요인뿐만 아니라 사회 경제적인 요인에 의한 영향을 고려하는 가뭄 취약성 평가가 수반되어야 한다. 본 연구에서는 지역별 가뭄 취약성 평가를 수행하기 위해, 지역별 용수 수요 및 공급관련 인자와 선행연구에서 정의된 가뭄 위험인자들 중 8개(생활 농업 공업 용수공급량, 인구밀도, 1인당 가용수자원량, 물 자급률, 취수율, 물 이용 공평성)를 선택하였다. 베이지안 네트워크(Bayesian Network) 기법을 통해 선정된 사회 경제적 요인들과 가뭄과의 상관관계를 분석하여 각 지역의 특성을 고려한 가뭄 위험요인별 확률을 산정하였다. 최종적으로 산정된 주요 가뭄 위험요인별 확률을 우선순위에 따른 가중치를 적용하여 지역별 가뭄 취약성지수(Drought Vulnerability Index, DVI)를 산정하였고, 이를 이용하여 우리나라의 행정구역별로 취약성 평가를 수행하고 지도로 표시하였다. 지역별 가뭄 취약성 평가를 수행한 결과 익산, 상주, 완주 순으로 높게 나타났으며, 계룡, 과천, 종로순으로 가장 낮게 산정되었다. 또한 광역자치단체의 평균 가뭄 취약성지수를 산정한 결과 전라북도 지역이 가장 높게 나타났으며, 대구 및 대전광역시가 가장 낮게 나타났다.

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