Markov Chains: From Theory to Implementation and Experimentation

By P. Gagniuc. 2017

542 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

GeniePath: Graph Neural Networks with Adaptive Receptive Paths (2018, methodology): “In case φ (A) = D−1A and we ignore the activation function σ , this leads toH (T ) = φ (A)TH (0)W 1 with theT -th order transition matrix (D−1A)T [5] as the pre-defined receptive field.”
BABEL: Bodies, Action and Behavior with English Labels (2021, unknown): “Concretely, we compute P ( a i | a i − 1 , a i − 2 , a i − 3 ) , an order 3 Markov Chain [9], and observe in Table 2 that random walks along this chain generate plausible action sequences for human movement.”
A comprehensive survey on machine learning for networking: evolution, applications and research opportunities (2018, methodology): “In addition, the Markov model [159, 296] (elaborated 50 years earlier) was leveraged to construct a process based on discrete-time state transitions and action rewards, named Markov Decision Process (MDP), which formalizes sequential decision-making problems in a fully observable, controlled environ”