References
[1]. W. J. Anderson. Continuous-Time Markov Chains. Springer-Verlag, New York, 1991.
[2]. R. J. Adler. The Geometry of Random Fields., Wiley, New York, 1981.
[3]. P. Baldi, L. Mazliak and P. Priouret. Martingales and Markov Chains: Solved Exercises and Elements of Theory. Chapman-Hall/CRC, Boca Raton, 2002.
[4]. I. V. Basawa and B. L. S. Prakasa Rao. Statistical Inference for Stochastic Processes. Academic Press, London.
[5]. P. Billingsley. Convergence of Probability Measures, 2nd Ed., Wiley, New York, 2000.
[6]. K. L. Chung. Markov Chains with Stationary Transition Probabilities, 2nd Ed. Springer-Verlag,
[7]. New York, 1967.
[8]. K. L. Chung and R. J. Williams. Introduction to Stochastic Integration, 2nd Ed. Birkhauser, Boston, 1990.
[9]. P. Embrechts and M. Maejima. Selfsimilar Processes. Princeton University Press, Princeton, 2002.
[10]. T. Hida and M. Hitsuda. Gaussian Processes. American Mathematical Society, Providence, R.I. , 1991.
[11]. O. Kallenberg. Random Measures. Academic Press, London, 1976.
[12]. I. Karatzas and S. E. Shreve. Brownian Motion and Stochastic Calculus. Springer-Verlag, New York, 1991.
[13]. E. Lukacs. Characteristic Functions, 2nd Ed. Griffin, 1970.
[14]. [14] S. P. Meyn and R. L. Tweedie. Markov Chains and Stochastic Stability. Springer-Verlag, New York, 1993.
[15]. V. V. Petrov. Limit Theorems of Probability Theory. Oxford University Press, Oxford, 1995.
[16]. G. Samorodnitsky and M. S. Taqqu. Stable Non-Gaussian Random Processes. Chapman-Hall/CRC, Boca Raton, 1994.
[17]. G. R. Shorack. Probability for Statisticians. Springer-Verlag, New York, 2000.
[18]. R. Durrett. Probability: theory and examples (Vol. 49). Cambridge university press, 2019.
[19]. A. Pfeffer. Practical probabilistic programming. Simon and Schuster, 2016.
Cite this article
Xiong,Y.;Wei,T. (2023). Probalistic model of spam filter system . Applied and Computational Engineering,6,277-282.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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References
[1]. W. J. Anderson. Continuous-Time Markov Chains. Springer-Verlag, New York, 1991.
[2]. R. J. Adler. The Geometry of Random Fields., Wiley, New York, 1981.
[3]. P. Baldi, L. Mazliak and P. Priouret. Martingales and Markov Chains: Solved Exercises and Elements of Theory. Chapman-Hall/CRC, Boca Raton, 2002.
[4]. I. V. Basawa and B. L. S. Prakasa Rao. Statistical Inference for Stochastic Processes. Academic Press, London.
[5]. P. Billingsley. Convergence of Probability Measures, 2nd Ed., Wiley, New York, 2000.
[6]. K. L. Chung. Markov Chains with Stationary Transition Probabilities, 2nd Ed. Springer-Verlag,
[7]. New York, 1967.
[8]. K. L. Chung and R. J. Williams. Introduction to Stochastic Integration, 2nd Ed. Birkhauser, Boston, 1990.
[9]. P. Embrechts and M. Maejima. Selfsimilar Processes. Princeton University Press, Princeton, 2002.
[10]. T. Hida and M. Hitsuda. Gaussian Processes. American Mathematical Society, Providence, R.I. , 1991.
[11]. O. Kallenberg. Random Measures. Academic Press, London, 1976.
[12]. I. Karatzas and S. E. Shreve. Brownian Motion and Stochastic Calculus. Springer-Verlag, New York, 1991.
[13]. E. Lukacs. Characteristic Functions, 2nd Ed. Griffin, 1970.
[14]. [14] S. P. Meyn and R. L. Tweedie. Markov Chains and Stochastic Stability. Springer-Verlag, New York, 1993.
[15]. V. V. Petrov. Limit Theorems of Probability Theory. Oxford University Press, Oxford, 1995.
[16]. G. Samorodnitsky and M. S. Taqqu. Stable Non-Gaussian Random Processes. Chapman-Hall/CRC, Boca Raton, 1994.
[17]. G. R. Shorack. Probability for Statisticians. Springer-Verlag, New York, 2000.
[18]. R. Durrett. Probability: theory and examples (Vol. 49). Cambridge university press, 2019.
[19]. A. Pfeffer. Practical probabilistic programming. Simon and Schuster, 2016.