Lumped Markov chain

A Lumped Markov chain records only a partition class of the original state. It is Markov when the Transition probability into every class is constant within each source class.

A function of the current state of a Markov chain can discard information needed to predict its next value. A sufficient condition preserving the Markov property is strong lumpability: all original states with the same recorded value must give the same transition probabilities to every recorded class.

For the two-column model in growth rate of two-column sticky deposition, the pair of column heights is Markov, but its maximum alone is not. Starting empty, the maximum-height histories $(1,1,2)$ and $(1,2,2)$ both end at height two. The former leaves unequal columns, so the next height increases with probability $1/2$; the latter leaves equal columns, so it increases with probability one. Each history has positive probability. Their different predictions at the same time and current value violate the Markov property. By contrast, the absolute height difference retains exactly the information needed for its own transitions.

For a triangular-lattice walk in a sixty-degree wedge with rate $1/6$ on interior edges and doubled outward boundary rates, the horizontal position is conditionally uniform on $\{0,\dots ,k\}$ given its current vertical level and past levels. Within level $k$, the reflecting horizontal generator is symmetric, while vertical killing has constant rate $2/3$, so the uniform law survives any holding period. Each column of the upward or downward transition-rate block has sum $1/3$ for $k>0$, yielding a uniform conditional law at the next level. The induced birth-death process has rates ${\lambda }_k=(k+2)/(3(k+1))$ and ${\mu }_k=k/(3(k+1))$. Its probability of ever hitting zero from level $k$ is $1/(k+1)$, so it is transient.