• Title/Summary/Keyword: recursive probability

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Computation of Blocking Probability in a Loss System

  • Na, Seong-Ryong
    • Proceedings of the Korean Statistical Society Conference
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    • 2005.05a
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    • pp.203-207
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    • 2005
  • A loss system where two types of customers arrive in accordance with two independent Poisson processes is considered. An efficient recursive formula is developed for calculating the loss probability when the number of servers is large. Some practical examples regarding the performance evaluation of telecommunications networks are discussed.

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Recursive Parameter estimation algorithm of the Probability (확률밀도함수의 축차모수추정 방법)

  • 한영열;박진수
    • Proceedings of the Korean Institute of Communication Sciences Conference
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    • 1984.04a
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    • pp.42-45
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    • 1984
  • we propose a new parameter estimation algorithm that converge with probability one and in mean square, If the mean is the function of parameter of the probability density function. This recursive algorithm is applicable also ever the parameters we estimate are multiparameter case. And the results are shown by the computer simulation.

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Recursive Estimation of Biased Zero-Error Probability for Adaptive Systems under Non-Gaussian Noise (비-가우시안 잡음하의 적응 시스템을 위한 바이어스된 영-오차확률의 반복적 추정법)

  • Kim, Namyong
    • Journal of Internet Computing and Services
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    • v.17 no.1
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    • pp.1-6
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    • 2016
  • The biased zero-error probability and its related algorithms require heavy computational burden related with some summation operations at each iteration time. In this paper, a recursive approach to the biased zero-error probability and related algorithms are proposed, and compared in the simulation environment of shallow water communication channels with ambient noise of biased Gaussian and impulsive noise. The proposed recursive method has significantly reduced computational burden regardless of sample size, contrast to the original MBZEP algorithm with computational complexity proportional to sample size. With this computational efficiency the proposed algorithm, compared with the block-processing method, shows the equivalent robustness to multipath fading, biased Gaussian and impulsive noise.

A Study on the Recursive Parameter Estimation Density Function Algorithm of the Probability (확률밀도합수의 축차모수추정방식에 관한 연구)

  • 한영렬;박진수
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.9 no.4
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    • pp.163-169
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    • 1984
  • We propose a new parameter estimation algorithm that converges with probability one and in mean square, if the mean is the function of parameter of the probability density function. This recursive algorithm is applicable also even though the parameters we estimate are multiparameter case. And the results are shown by the computer simulation.

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Recursive Unscented Kalman Filtering based SLAM using a Large Number of Noisy Observations

  • Lee, Seong-Soo;Lee, Suk-Han;Kim, Dong-Sung
    • International Journal of Control, Automation, and Systems
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    • v.4 no.6
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    • pp.736-747
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    • 2006
  • Simultaneous Localization and Map Building(SLAM) is one of the fundamental problems in robot navigation. The Extended Kalman Filter(EKF), which is widely adopted in SLAM approaches, requires extensive computation. The conventional particle filter also needs intense computation to cover a high dimensional state space with particles. This paper proposes an efficient SLAM method based on the recursive unscented Kalman filtering in an environment including a large number of landmarks. The posterior probability distributions of the robot pose and the landmark locations are represented by their marginal Gaussian probability distributions. In particular, the posterior probability distribution of the robot pose is calculated recursively. Each landmark location is updated with the recursively updated robot pose. The proposed method reduces filtering dimensions and computational complexity significantly, and has produced very encouraging results for navigation experiments with noisy multiple simultaneous observations.

Speech Enhancement Based on Minima Controlled Recursive Averaging Technique Incorporating Conditional MAP (조건 사후 최대 확률 기반 최소값 제어 재귀평균기법을 이용한 음성향상)

  • Kum, Jong-Mo;Park, Yun-Sik;Chang, Joon-Hyuk
    • The Journal of the Acoustical Society of Korea
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    • v.27 no.5
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    • pp.256-261
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    • 2008
  • In this paper, we propose a novel approach to improve the performance of minima controlled recursive averaging (MCRA) which is based on the conditional maximum a posteriori criterion. A crucial component of a practical speech enhancement system is the estimation of the noise power spectrum. One state-of-the-art approach is the minima controlled recursive averaging (MCRA) technique. The noise estimate in the MCRA technique is obtained by averaging past spectral power values based on a smoothing parameter that is adjusted by the signal presence probability in frequency subbands. We improve the MCRA using the speech presence probability which is the a posteriori probability conditioned on both the current observation the speech presence or absence of the previous frame. With the performance criteria of the ITU-T P.862 perceptual evaluation of speech quality (PESQ) and subjective evaluation of speech quality, we show that the proposed algorithm yields better results compared to the conventional MCRA-based scheme.

A New Calculation Method of Equalizer algorithms based on the Probability Correlation (확률분포 상관도에 기반한 Equalizer 알고리듬의 새로운 연산 방식)

  • Kim, Namyong
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.15 no.5
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    • pp.3132-3138
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    • 2014
  • In many communication systems, intersymbol interference, DC and impulsive noise are hard-to-solve problems. For the purpose of cancelling such interferences, the concept of lagged cross-correlation of probability has been used for blind equalization. However, this algorithm has a large burden of computation. In this paper, a recursive method of the algorithm based on the lagged probability correlation is proposed. The summation operation in the calculation of gradient of the cost is transformed into a recursive gradient calculation. The recursive method shows to reduce the high computational complexity of the algorithm from O(NM) to O(M) for M symbols and N block data having advantages in implementation while keeping the robustness against those interferences. From the results of the simulation, the proposed method yields the same learning performance with reduced computation complexity.

Recursive Probability Estimation of Decision Feedback Equalizers based on Constant Modulus Errors (상수 모듈러스 오차의 반복적 확률추정에 기반한 결정궤환 등화)

  • Kim, Namyong
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.16 no.3
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    • pp.2172-2177
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    • 2015
  • The DF-MZEP-CME (decision feedback - maximum zero-error probability for constant modulus errors) algorithm that makes the probability for constant modulus error (CME) close to zero and employs decision feedback (DF) structures shows more improved performance in channel distortion compensation. However the DF-MZEP-CME algorithm has a computational complexity proportional to a sample size for probability estimation and this property plays a role of an obstacle in practical implementation. In this paper, the gradient of DF-MZEP-CME is proposed to be estimated recursively and shown to solve the computational problem by making the algorithm independent of the sample size. For a sample size N, the conventional method has 10N multiplications but the proposed has only 20 regardless of N. Also the recursive gradient estimation for weight update is kept in continuity from the initial state to the steady state without any error propagation.

Sequential Confidence Intervals for Quantiles Based on Recursive Density Estimators

  • Kim, Sung-Kyun;Kim, Sung-Lai
    • Journal of the Korean Statistical Society
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    • v.28 no.3
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    • pp.297-309
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    • 1999
  • A sequential procedure of fixed-width confidence intervals for quantiles satisfying a condition of coverage probability is provided based on recursive density estimators. It is shown that the proposed sequential procedure is asymptotically efficient. In addition, the asymptotic normality for the proposed stopping time is derived.

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NOTE ON THE OUTDEGREE OF A NODE IN RANDOM RECURSIVE TREES

  • Javanian, Mehri
    • Journal of applied mathematics & informatics
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    • v.13 no.1_2
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    • pp.99-103
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    • 2003
  • In this note we find the exact Probability distribution of d$\_$n, i/ the outdegree of the node i, in a random recursive tree with n nodes. For i = i$\_$n/ increasing as a linear function on n, we show that d$\_$n/,i$\_$n/ is asymptotically normal.