935 resultados para equação de Poisson


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In previous Statnotes, many of the statistical tests described rely on the assumption that the data are a random sample from a normal or Gaussian distribution. These include most of the tests in common usage such as the ‘t’ test ), the various types of analysis of variance (ANOVA), and Pearson’s correlation coefficient (‘r’) . In microbiology research, however, not all variables can be assumed to follow a normal distribution. Yeast populations, for example, are a notable feature of freshwater habitats, representatives of over 100 genera having been recorded . Most common are the ‘red yeasts’ such as Rhodotorula, Rhodosporidium, and Sporobolomyces and ‘black yeasts’ such as Aurobasidium pelculans, together with species of Candida. Despite the abundance of genera and species, the overall density of an individual species in freshwater is likely to be low and hence, samples taken from such a population will contain very low numbers of cells. A rare organism living in an aquatic environment may be distributed more or less at random in a volume of water and therefore, samples taken from such an environment may result in counts which are more likely to be distributed according to the Poisson than the normal distribution. The Poisson distribution was named after the French mathematician Siméon Poisson (1781-1840) and has many applications in biology, especially in describing rare or randomly distributed events, e.g., the number of mutations in a given sequence of DNA after exposure to a fixed amount of radiation or the number of cells infected by a virus given a fixed level of exposure. This Statnote describes how to fit the Poisson distribution to counts of yeast cells in samples taken from a freshwater lake.

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2010 Mathematics Subject Classification: 60J80.

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2010 Mathematics Subject Classification: 60E05, 62P05.

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2000 Mathematics Subject Classification: 60K10, 62P05.

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2000 Mathematics Subject Classification: 35Q02, 35Q05, 35Q10, 35B40.

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2010 Mathematics Subject Classification: 17A32, 17B63.

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This paper explains how Poisson regression can be used in studies in which the dependent variable describes the number of occurrences of some rare event such as suicide. After pointing out why ordinary linear regression is inappropriate for treating dependent variables of this sort, we go on to present the basic Poisson regression model and show how it fits in the broad class of generalized linear models. Then we turn to discussing a major problem of Poisson regression known as overdispersion and suggest possible solutions, including the correction of standard errors and negative binomial regression. The paper ends with a detailed empirical example, drawn from our own research on suicide.

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Mémoire numérisé par la Direction des bibliothèques de l'Université de Montréal.

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Mémoire numérisé par la Direction des bibliothèques de l'Université de Montréal.

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A description of a whale shark (Rhincodon typus) captured on June 1934 along the coast of Cap Ti Oan is given. The scientific name Rhincodon is not correct due to a typographic error. The name with greek origin, should be: Rhineodon typus.

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Cette étude a été réalisée par le WorldFish Center dans le cadre de sa collaboration avec le WWF et le projet CARPE de l’USAID. La chaîne de commercialisation du poisson a été suivie des zones de production jusqu’au principal marché final. La région au centre de cette étude est le lac Ntomba ainsi qu’une partie du fleuve Congo. Ceci correspond à la partie nord de la section du Paysage Lac Télé - Lac Tumba de la République Démocratique du Congo. Toutes les étapes de la chaîne de commercialisation du poisson ont été prises en compte dans cette étude. Les stratégies des acteurs présents à chaque étape ainsi que les dynamiques les associant à la chaîne ont été utilisées pour refléter le fonctionnement de la chaîne de commercialisation du poisson et identifier ses spécificités. La pêche est une activité saisonnière de grande importance dans la région du lac Ntomba et la partie proche du fleuve Congo. La majorité des habitants de la région constituent leurs moyens d’existence d’un panachage d’activités, dont la séquence semble être rythmée essentiellement par l’opportunité et le climat. Presque aucun service n’est offert dans cette région où les activités économiques sont réduites et n’ont le plus souvent qu’une faible productivité. Cependant, la chaîne de commercialisation du poisson supporte un large éventail d’acteurs différents et représente un secteur d’une grande importance pour la région. Cette région périphérique est reliée au reste de l’économie nationale par une série de marchés et de nombreux différents types d’acteurs. Assurer ce lien représente de sérieuses difficultés et comporte des risques importants tandis que les marges de profits sont le plus souvent minces. Cependant, il semblerait que, de manière générale, le marché se développe et les liens se renforcent, même si cette évolution est plus discrète dans les lieux les plus isolés.

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This paper deals with the development and the analysis of asymptotically stable and consistent schemes in the joint quasi-neutral and fluid limits for the collisional Vlasov-Poisson system. In these limits, the classical explicit schemes suffer from time step restrictions due to the small plasma period and Knudsen number. To solve this problem, we propose a new scheme stable for choices of time steps independent from the small scales dynamics and with comparable computational cost with respect to standard explicit schemes. In addition, this scheme reduces automatically to consistent discretizations of the underlying asymptotic systems. In this first work on this subject, we propose a first order in time scheme and we perform a relative linear stability analysis to deal with such problems. The framework we propose permits to extend this approach to high order schemes in the next future. We finally show the capability of the method in dealing with small scales through numerical experiments.

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