## The Annals of Probability

- Ann. Probab.
- Volume 4, Number 2 (1976), 313-318.

### Central Terms of Markov Walks

#### Abstract

A $\{0, 1\}$-valued discrete time stochastic process $\beta = \{\beta_n\}^\infty_{n=1}$ will be referred to simply as a walk. The notion of central (modal) term of a binomial distribution is generalized to the conditional-on-the-past distributions of $N$th partial sums of walks. The emphasis here is placed on the smallest possible central term $V_A(N)$ within a given class $A$ of walks. If $A$ consists of (i) all walks, (ii) all stationary independent walks, (iii) all stationary Markov walks which are invariant under interchange of 0 and 1, then, respectively, (i) $\{N \cdot V_A(N)\}^\infty_{N=1}$, (ii) $\{N^{\frac{1}{2}} \cdot V_A(N)\}^\infty_{N=1}$, (iii) $\{N \cdot V_A(N)/(\log N)^{\frac{1}{2}}\}^\infty_{N=2}$ are bounded sequences which are bounded away from zero.

#### Article information

**Source**

Ann. Probab. Volume 4, Number 2 (1976), 313-318.

**Dates**

First available in Project Euclid: 19 April 2007

**Permanent link to this document**

https://projecteuclid.org/euclid.aop/1176996136

**Digital Object Identifier**

doi:10.1214/aop/1176996136

**Mathematical Reviews number (MathSciNet)**

MR423539

**Zentralblatt MATH identifier**

0348.60052

**JSTOR**

links.jstor.org

**Subjects**

Primary: 60G17: Sample path properties

Secondary: 60G25: Prediction theory [See also 62M20] 60J10: Markov chains (discrete-time Markov processes on discrete state spaces) 62M20: Prediction [See also 60G25]; filtering [See also 60G35, 93E10, 93E11] 60C05: Combinatorial probability

**Keywords**

Central term Markov walk

#### Citation

Myers, L. E. Central Terms of Markov Walks. Ann. Probab. 4 (1976), no. 2, 313--318. doi:10.1214/aop/1176996136. https://projecteuclid.org/euclid.aop/1176996136