957 resultados para Branching Processes with Immigration
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2000 Mathematics Subject Classification: 60J80.
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The classical Bienaymé-Galton-Watson (BGW) branching process can be interpreted as mathematical model of population dynamics when the members of an isolated population reproduce themselves independently of each other according to a stochastic law.
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2000 Mathematics Subject Classification: 60J80, 62M05
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2000 Mathematics Subject Classification: 60J80, 60F05
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AMS subject classification: 60J80, 60J15.
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2000 Mathematics Subject Classification: 60J80, 60F05
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The paper is a contribution to the theory of branching processes with discrete time and a general phase space in the sense of [2]. We characterize the class of regular, i.e. in a sense sufficiently random, branching processes (Φk) k∈Z by almost sure properties of their realizations without making any assumptions about stationarity or existence of moments. This enables us to classify the clans of (Φk) into the regular part and the completely non-regular part. It turns out that the completely non-regular branching processes are built up from single-line processes, whereas the regular ones are mixtures of left-tail trivial processes with a Poisson family structure.
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2000 Mathematics Subject Classification: 60J80
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2000 Mathematics Subject Classification: 60J80.
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2000 Mathematics Subject Classification: 60J80, 62M05
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A new structure with the special property that instantaneous resurrection and mass disaster are imposed on an ordinary birth-death process is considered. Under the condition that the underlying birth-death process is exit or bilateral, we are able to give easily checked existence criteria for such Markov processes. A very simple uniqueness criterion is also established. All honest processes are explicitly constructed. Ergodicity properties for these processes are investigated. Surprisingly, it can be proved that all the honest processes are not only recurrent but also ergodic without imposing any extra conditions. Equilibrium distributions are then established. Symmetry and reversibility of such processes are also investigated. Several examples are provided to illustrate our results.
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2000 Mathematics Subject Classification: Primary 60J80, Secondary 62F12, 60G99.
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2000 Mathematics Subject Classification: 60J80, 60G70.
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The power-law size distributions obtained experimentally for neuronal avalanches are an important evidence of criticality in the brain. This evidence is supported by the fact that a critical branching process exhibits the same exponent t~3=2. Models at criticality have been employed to mimic avalanche propagation and explain the statistics observed experimentally. However, a crucial aspect of neuronal recordings has been almost completely neglected in the models: undersampling. While in a typical multielectrode array hundreds of neurons are recorded, in the same area of neuronal tissue tens of thousands of neurons can be found. Here we investigate the consequences of undersampling in models with three different topologies (two-dimensional, small-world and random network) and three different dynamical regimes (subcritical, critical and supercritical). We found that undersampling modifies avalanche size distributions, extinguishing the power laws observed in critical systems. Distributions from subcritical systems are also modified, but the shape of the undersampled distributions is more similar to that of a fully sampled system. Undersampled supercritical systems can recover the general characteristics of the fully sampled version, provided that enough neurons are measured. Undersampling in two-dimensional and small-world networks leads to similar effects, while the random network is insensitive to sampling density due to the lack of a well-defined neighborhood. We conjecture that neuronal avalanches recorded from local field potentials avoid undersampling effects due to the nature of this signal, but the same does not hold for spike avalanches. We conclude that undersampled branching-process-like models in these topologies fail to reproduce the statistics of spike avalanches.
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Im Verzweigungsprozess mit Immigration werden Schätzer für die erwartete Nachkommenzahl m eines Individuums und die erwartete Immigration λ pro Generation konstruiert. Sie sind nur aufgrund der beobachteten Populationsgröße einer jeden Generation konsistent, ohne Vorkenntnis darüber, ob der Prozess subkritisch (m<1), kritisch (m=1) oder superkritisch (m>1) ist. Im superkritischen Fall ist der Schätzer für λ jedoch nicht konsistent. Dies ist aber keine Einschränkung, denn es wird gezeigt, dass in diesem Fall kein konsistenter Schätzer für λ existiert. Des Weiteren werden Konvergenzgeschwindigkeit der Schätzer und asymptotische Verteilungen der Schätzfehler untersucht. Dabei werden die Fälle (m<1), (m>1) und (m=1) unterschieden, was gänzlich verschiedene Vorgehensweisen erfordert (Ergodizität, Martingalmethoden, Diffusionsapproximationen). Diese hier vorliegende Diplomarbeit orientiert sich an den Ideen und Ergebnissen von Wei und Winnicki (1989/90).