L. Bremer, “Markov chain Monte Carlo estimation of nonlinear dynamics from time series,” Physica D , vol. 160, no. 1–2, pp. 116–126, 2001.C. L. Bremer and D. T. Kaplan, "Markov chain Monte Carlo estimation of nonlinear dynamics from time series," Physica D 160(1-2), pp. ...
It is common practice in Markov chain Monte Carlo to update the simulation one variable (or sub-block of variables) at a time, rather than conduct a single... AA Johnson,GL Jones,RC Neath - 《Statistical Science》 被引量: 50发表: 2013年 Stability of nonlinear AR(1) time series with ...
A Markov chain (discrete-time Markov chain or DTMC[1]), named after Andrey Markov, is a random process that undergoes transitions from one state to another on a state space. It must possess a property that is usually characterized as "memoryless": the probability distribution of the next ...
Here is an example of how a Markov chain might be used to model the evolution of a time series: Suppose we have a time series of stock prices, and we want to use a Markov chain to model the evolution of the stock's price over time. We can define a set of states that the stock'...
The past GPEC pattern can be modelled as Markov chain process along discrete time series summarized as follows. Assume that Xn (n ≥ 0) is an arbitrary process with the state space of Sn (n ϵ N); the arbitrary process can be called a Markov chain if the probability of (1) is...
Markov chain Monte Carlononlinear dynamicschaotic systemsBayesian analysistime seriesWe compare dynamical properties of brain electrical activity from different ... RG Andrzejak,K Lehnertz,F Mormann,... - 《Phys Rev E Stat Nonlin Soft Matter Phys》 被引量: 1656发表: 2001年 Dynamics from multivari...
Markov chains only work when the states are discrete. Text satisfies this property, since there are a finite number of characters in a sequence. If you have a continuous time series then Markov chains can't be used. To define a Markov chain we need to specify an alphabet i.e. the possi...
The Markov property expresses that the likelihood of changing to a specific state is reliant exclusively on the present state and elapsed time and not on the series of states that have preceded it. This distinctive characteristic of Markov process imparts them to be memoryless. Markov chain is ...
This paper evaluates the small and large sample properties of Markov chain time-dependence and time-homogeneity tests. First, we present the Markov chain methodology to investigate various statistical properties of time series. Considering an auto-regressive time series and its associated Markov chain ...
In this article, we will discuss what happens to the Transition Matrix when we takea large number of discrete time steps. In other words, we will describe how the Markov Chain develops as timetends to infinity. Markov Chain and Transition Matrix ...