This document is a comparative study of four methods (Markov chains, compound processes, substructure factorization and generating functions) to compute the moments of probability distributions associated to homogeneous branching processes. Although all these methods have their own interests, the generating functions seem to be the most appropriate tools for this kind of probability model. In order to compare them, each method is first described and then applied to an example of multitype branching processes: the evolution of the number of active buds for a particular GreenLab plant growth model. Finally, the methods are listed according to their effectiveness
Here we derive and solve the Kolmogorov backward equations of the two-type branching process necessa...
The current paper surveys and develops numerical methods for Markovian multitype branching processes...
International audienceIf the interest of stochastic L-systems for plant growth simulation and visual...
This document is a comparative study of four methods (Markov chains, compound processes, substructur...
This document is a comparative study of four methods (Markov chains, compound processes, substructur...
This document is a comparative study of four methods (Markov chains, compound processes, substructur...
Generating functions are suitable mathematical apparatus to describe the distribution of random vari...
This book provides a theoretical background of branching processes and discusses their biological ap...
This volume gathers papers originally presented at the 3rd Workshop on Branching Processes and their...
If the interest of stochastic L-systems for plant growth simulation and visualization is broadly ack...
If the interest of stochastic L-systems for plant growth simulation and visualization is broadly ack...
In this work we provide a survey on the main probabilistic contributions derived in the literature f...
If the interest of stochastic L-systems for plant growth simulation and visualization is broadly ack...
Multitype branching processes (MTBP) model branching structures, where the nodes of the resulting tr...
We study the behavior of branching process in a random environment on trees in the critical, subcrit...
Here we derive and solve the Kolmogorov backward equations of the two-type branching process necessa...
The current paper surveys and develops numerical methods for Markovian multitype branching processes...
International audienceIf the interest of stochastic L-systems for plant growth simulation and visual...
This document is a comparative study of four methods (Markov chains, compound processes, substructur...
This document is a comparative study of four methods (Markov chains, compound processes, substructur...
This document is a comparative study of four methods (Markov chains, compound processes, substructur...
Generating functions are suitable mathematical apparatus to describe the distribution of random vari...
This book provides a theoretical background of branching processes and discusses their biological ap...
This volume gathers papers originally presented at the 3rd Workshop on Branching Processes and their...
If the interest of stochastic L-systems for plant growth simulation and visualization is broadly ack...
If the interest of stochastic L-systems for plant growth simulation and visualization is broadly ack...
In this work we provide a survey on the main probabilistic contributions derived in the literature f...
If the interest of stochastic L-systems for plant growth simulation and visualization is broadly ack...
Multitype branching processes (MTBP) model branching structures, where the nodes of the resulting tr...
We study the behavior of branching process in a random environment on trees in the critical, subcrit...
Here we derive and solve the Kolmogorov backward equations of the two-type branching process necessa...
The current paper surveys and develops numerical methods for Markovian multitype branching processes...
International audienceIf the interest of stochastic L-systems for plant growth simulation and visual...