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The relationship between the complete convergence and the strong law of large numbers is established.
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The completion of the BI was determined when the average standard deviation of split frequencies ≤0.05 (Ronquist and Huelsenbeck 2003) for the combined two runs was assumed and the complete convergence between the Bayesian Markov chain Monte Carlo runs was reached.
Chow [6] studied the complete convergence of,.
It is well known that the complete moment convergence can imply the complete convergence.
It is well known that the complete moment convergence implies the complete convergence.
Hence, from (2.7), one can clearly know that the complete moment convergence implies the complete convergence.
Hence, from (2.4) we get that the complete moment convergence implies the complete convergence.
The complete moment convergence is a more general version of the complete convergence.
As applications, we study the complete convergence for it.
However, we may have the complete convergence without this assumption.
In this paper, we establish the complete convergence and complete moment convergence and obtain the equivalence of the complete convergence and complete moment convergence for the class of random variables satisfying a Rosenthal-type maximal inequality.
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