• Title, Summary, Keyword: Markov

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Incremental-runlength distribution for Markov graphic data source (Markov 그라픽 데이타에 대한 incremental-runlength의 확률분포)

  • 김재균
    • 전기의세계
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    • v.29 no.6
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    • pp.389-392
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    • 1980
  • For Markov graphic source, it is well known that the conditional runlength coding for the runs of correct prediction is optimum for data compression. However, because of the simplicity in counting and the stronger concentration in distrubution, the incremental run is possibly a better parameter for coding than the run itself for some cases. It is shown that the incremental-runlength is also geometrically distributed as the runlength itself. The distribution is explicitly described with the basic parameters defined for a Markov model.

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A Study on the Entropy of Binary First Order Markov Information Source (이진 일차 Markov 정보원의 엔트로피에 관한 연구)

  • 송익호;안수길
    • Journal of the Korean Institute of Telematics and Electronics
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    • v.20 no.2
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    • pp.16-22
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    • 1983
  • In this paper, we obtained PFME(probability for maximum entropy) and entropy when a conditional probability was given in a binary list order Markov Information Source. And, when steady state probability was constant, the influence of change of a conditional probability on entropy was examined, too.

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The Realization of Artificial Life to Adapt The Environment by Using The Markov Model

  • Kim, Do-Wan;Park, Wong-Hun;Chung, Jin-Wook;Hoon Kang
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • pp.513-516
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    • 2003
  • In this paper, we designed a Artificial Life(AL) that acts the appropriate actions according to the user's action, environments and AL's feeling. To realize this AL, we used the Markov Model. We consisted of the chromosome by Markov Model and obtained the appropriate actions by Genetic Algorithm.

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Balanced mobility pattern generation using Random Mean Degree modification in Gauss Markov model for Mobile network (이동 네트워크를 위한 가우스 마코프 모델에서 평균 이동각도 조절을 통한 균형잡힌 이동 패턴 생성)

  • 노재환;이병직;류정필;하남구;한기준
    • Proceedings of the Korean Information Science Society Conference
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    • pp.502-504
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    • 2004
  • 이동성이 중요시되는 네트워크에서 특정 프로토콜의 성능 평가를 위해서는 노드의 이동패턴을 정확하게 표현할 수 있는 Mobility Model이 필요하다. 노드의 연속적인 이동패턴을 필요로 하는 Mobile Ad-hoc 네트워크를 위해선 Markov process 기반의 Gauss-Markov Mobility Model이 적절하다. 그러나 맵의 엣지 부근에서 노드 이동의 부적절한 처리로 인해, 기존의 Gauss-Markov Model은 편중된 이동 패턴을 야기한다. 본 논문은 엣지 부근의 평균 이동각도를 랜덤하게 조정함으로써 기존의 모델이 가진 문제를 해결하고, 시뮬레이션을 통해서 이를 검증한다.

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Two-Dimensional Model of Hidden Markov Mesh

  • Sin, Bong-Kee
    • 한국HCI학회:학술대회논문집
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    • pp.772-779
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    • 2006
  • The new model proposed in this paper is the hidden Markov mesh model or the 2D HMM with the causality of top-down and left-right direction. With the addition of the causality constraint, two algorithms for the evaluation of a model and the maximum likelihood estimation of model parameters have been developed theoretically which are based on the forward-backward algorithm. It is a more natural extension of the 1D HMM than other 2D models. The proposed method will provide a useful way of modeling highly variable image patterns such as offline cursive characters.

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A Soccer Video Analysis Using Product Hierarchical Hidden Markov Model (PHHMM(Product Hierarchical Hidden Markov Model)을 이용한 축구 비디오 분석)

  • Kim, Moo-Sung;Kang, Hang-Bong
    • Proceedings of the IEEK Conference
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    • pp.681-682
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    • 2006
  • 일반적으로 축구 비디오 데이터는 멀티모달과 멀티레이어 속성을 지닌다. 이러한 데이터를 다루기 적합한 모델은 동적 베이지안 네트워크(Dynamic Bayesian Network: DBN) 형태의 위계적 은닉 마르코프 모델(Hierarchical Hidden Markov Model: HHMM)이다. 이러한 HHMM 중 다중속성의 특징들이 서로 상호작용하는 PHHMM(Product Hierarchical Hidden Markov Model)이 있다. 본 논문에서는 PHHMM 을 축구 경기의 Play/Break 이벤트 검색 및 분석에 적용하였고 바람직한 결과를 얻었다.

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Waiting Times in Polling Systems with Markov-Modulated Poisson Process Arrival

  • Kim, D. W.;W. Ryu;K. P. Jun;Park, B. U.;H. D. Bae
    • Journal of the Korean Statistical Society
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    • v.26 no.3
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    • pp.355-363
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    • 1997
  • In queueing theory, polling systems have been widely studied as a way of serving several stations in cyclic order. In this paper we consider Markov-modulated Poisson process which is useful for approximating a superposition of heterogeneous arrivals. We derive the mean waiting time of each station in a polling system where the arrival process is modeled by a Markov-modulated Poisson process.

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Asymptotics of a class of markov processes generated by $X_{n+1}=f(X_n)+\epsilon_{n+1}$

  • Lee, Oe-Sook
    • Journal of the Korean Statistical Society
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    • v.23 no.1
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    • pp.1-12
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    • 1994
  • We consider the markov process ${X_n}$ on R which is genereated by $X_{n+1} = f(X_n) + \epsilon_{n+1}$. Sufficient conditions for irreducibility and geometric ergodicity are obtained for such Markov processes. In additions, when ${X_n}$ is geometrically ergodic, the functional central limit theorem is proved for every bounded functions on R.

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SOME LIMIT THEOREMS FOR POSITIVE RECURRENT AGE-DEPENDENT BRANCHING PROCESSES

  • Kang, Hye-Jeong
    • Journal of the Korean Mathematical Society
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    • v.38 no.1
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    • pp.25-35
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    • 2001
  • In this paper we consider an age dependent branching process whose particles move according to a Markov process with continuous state space. The Markov process is assumed to the stationary with independent increments and positive recurrent. We find some sufficient conditions for he Markov motion process such that the empirical distribution of the positions converges to the limiting distribution of the motion process.

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Generalized Maximum Likelihood Estimation in a Multistate Stochastic Model

  • Yeo, Sung-Chil
    • Journal of the Korean Operations Research and Management Science Society
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    • v.14 no.1
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    • pp.1-15
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    • 1989
  • Multistate survival data with censoring often arise in biomedical experiments. In particular, a four-state space is used for cancer clinical trials. In a four-state space, each patient may either respond to a given treatment and then relapse or may progress without responding. In this four-state space, a model which combines the Markov and semi-Markov models is proposed. In this combined model, the generalized maximum likelihood estimators of the Markov and semi-Markov hazard functions are derived. These estimators are illustrated for the data collected in a study of treatments for advanced breast cancer.

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