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Low-rank tensor methods for Markov chains with applications to tumor progression models

Georg, Peter ; Grasedyck, Lars ; Klever, Maren ; Schill, Rudolf ; Spang, Rainer ; Wettig, Tilo



Abstract

Cancer progression can be described by continuous-time Markov chains whose state space grows exponentially in the number of somatic mutations. The age of a tumor at diagnosis is typically unknown. Therefore, the quantity of interest is the time-marginal distribution over all possible genotypes of tumors, defined as the transient distribution integrated over an exponentially distributed ...

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