990 resultados para POISSON-DISTRIBUTION


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The spatial arrangement patterns of senile plaques have been studied in 10 micron cresyl violet stained sections cut from embedded portions of 20 brain regions from SDAT brains. Two studies are reported: an initial study using the Poisson distribution and a subsequent study using pattern analysis. The initial study indicated that plaques are arranged in discrete clumps in all brain regions when examined at x100 and x400 – suggesting that both small and larger scale clumping may be present. The pattern analysis study was applied to 8 cortical regions. This technique allows a more detailed study of pattern to be made. In all regions the technique revealed that the basic pattern of plaque arrangement is the regularly spaced discrete clump – which may be present on both large and small scales.

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We have studied the spatial distribution of plaques in coronal and tangential sections of the parahippocampal gyrus (PHG), the hippocampus, the frontal lobe and the temporal lobe of five SDAT patients. Sections were stained with cresyl violet and examined at two magnifications (x100 and x400). in all cases (and at both magnifications) statistical analysis using the Poisson distribution showed that the plaques were arranged in clumps (x100: V/M = 1.48 - 4.49; x400 V/M = 1.17 - 1.95). this indicates that both large scale and small scale clumping occurs. Application of the statistical techniques of pattern analysis to coronal sections of frontal and temporal cortex and PHG showed. furthermore, that both large (3200-6400 micron) and small scale (100 - 400 micron) clumps were arranged with a high degree of regularity in the tissue. This suggests that the clumps of plaques reflect underlying neural structure.

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Erbium-doped fibre amplifiers (EDFA’s) are a key technology for the design of all optical communication systems and networks. The superiority of EDFAs lies in their negligible intermodulation distortion across high speed multichannel signals, low intrinsic losses, slow gain dynamics, and gain in a wide range of optical wavelengths. Due to long lifetime in excited states, EDFAs do not oppose the effect of cross-gain saturation. The time characteristics of the gain saturation and recovery effects are between a few hundred microseconds and 10 milliseconds. However, in wavelength division multiplexed (WDM) optical networks with EDFAs, the number of channels traversing an EDFA can change due to the faulty link of the network or the system reconfiguration. It has been found that, due to the variation in channel number in the EDFAs chain, the output system powers of surviving channels can change in a very short time. Thus, the power transient is one of the problems deteriorating system performance. In this thesis, the transient phenomenon in wavelength routed WDM optical networks with EDFA chains was investigated. The task was performed using different input signal powers for circuit switched networks. A simulator for the EDFA gain dynamicmodel was developed to compute the magnitude and speed of the power transients in the non-self-saturated EDFA both single and chained. The dynamic model of the self-saturated EDFAs chain and its simulator were also developed to compute the magnitude and speed of the power transients and the Optical signal-to-noise ratio (OSNR). We found that the OSNR transient magnitude and speed are a function of both the output power transient and the number of EDFAs in the chain. The OSNR value predicts the level of the quality of service in the related network. It was found that the power transients for both self-saturated and non-self-saturated EDFAs are close in magnitude in the case of gain saturated EDFAs networks. Moreover, the cross-gain saturation also degrades the performance of the packet switching networks due to varying traffic characteristics. The magnitude and the speed of output power transients increase along the EDFAs chain. An investigation was done on the asynchronous transfer mode (ATM) or the WDM Internet protocol (WDM-IP) traffic networks using different traffic patterns based on the Pareto and Poisson distribution. The simulator is used to examine the amount and speed of the power transients in Pareto and Poisson distributed traffic at different bit rates, with specific focus on 2.5 Gb/s. It was found from numerical and statistical analysis that the power swing increases if the time interval of theburst-ON/burst-OFF is long in the packet bursts. This is because the gain dynamics is fast during strong signal pulse or with long duration pulses, which is due to the stimulatedemission avalanche depletion of the excited ions. Thus, an increase in output power levelcould lead to error burst which affects the system performance.

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Two morphological types of prion protein (PrPsc) deposit occur in the cerebral cortex of cases of variant Creutzfeldt-Jakob disease (vCJD), viz., diffuse and florid deposits. The objective of this study was to determine whether diffuse-type PrPsc deposits in areas of the cerebral cortex in six cases of the variant form of CJD (vCJD) were spatially correlated with neurons and whether diffuse deposit size was related to the number of adjacent neurons contributing PrPsc. In cortical gyri, density of surviving neurons was 5.38-12.15 per 50 × 200 µm sample field, neurons being distributed randomly, regularly or were clustered relative to the pia mater. Density of neurons embedded within diffuse deposits, however, was three to eight times their overall density in the section. In addition, diffuse deposit area was positively correlated with the number of embedded neurons. The frequency distribution of diffuse deposits with 0, 1, 2, 3, …, n, embedded neurons did not deviate from a Poisson distribution. These results suggest: (1) diffuse deposits in vCJD develop in situ as a result of the formation of PrPsc in relation to clusters of neurons, (2) size of a diffuse deposit is determined by the number of adjacent neurons which develop PrPsc, and (3) the probability that PrPsc is formed in relation to one neuron is independent of that of its neighbour.

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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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The aim of this paper is to establish some mixture distributions that arise in stochastic processes. Some basic functions associated with the probability mass function of the mixture distributions, such as k-th moments, characteristic function and factorial moments are computed. Further we obtain a three-term recurrence relation for each established mixture distribution.

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Dependence in the world of uncertainty is a complex concept. However, it exists, is asymmetric, has magnitude and direction, and can be measured. We use some measures of dependence between random events to illustrate how to apply it in the study of dependence between non-numeric bivariate variables and numeric random variables. Graphics show what is the inner dependence structure in the Clayton Archimedean copula and the Bivariate Poisson distribution. We know this approach is valid for studying the local dependence structure for any pair of random variables determined by its empirical or theoretical distribution. And it can be used also to simulate dependent events and dependent r/v/’s, but some restrictions apply. ACM Computing Classification System (1998): G.3, J.2.

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A dolgozat célja egy vállalati gyakorlatból származó eset elemzése. Egy könyvkiadót tekintünk. A kiadó kapcsolatban van kis- és nagykereskedőkkel, valamint a fogyasztók egy csoportjával is vannak kapcsolatai. A könyvkiadók projekt rendszerben működnek. A kiadó azzal a problémával szembesül, hogy hogyan ossza el egy frissen kiadott és nyomtatott könyv példányszámait a kis- és nagykereskedők között, valamint mekkora példányszámot tároljon maga a fogyasztók közvetlen kielégítésére. A kiadóról feltételezzük, hogy visszavásárlási szerződése van a kereskedőkkel. A könyv iránti kereslet nem ismert, de becsülhető. A kis- és nagykereskedők maximalizálják a nyereségüket. = The aim of the paper is to analyze a practical real world problem. A publishing house is given. The publishing firm has contacts to a number of wholesaler / retailer enterprises and direct contact to customers to satisfy the market demand. The book publishers work in a project industry. The publisher faces with the problem how to allocate the stocks of a given, newly published book to the wholesaler and retailer, and to hold some copies to satisfy the customers direct from the publisher. The publisher has a buyback option. The distribution of the demand is unknown, but it can be estimated. The wholesaler / retailer maximize the profits. The problem can be modeled as a one-warehouse and N-retailer supply chain with not identical demand distribution. The model can be transformed in a game theory problem. It is assumed that the demand distribution follows a Poisson distribution.

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Egy könyvkiadó vállalatot vizsgálunk. A kiadó kiadványait a szokásos értékesítési láncon (kis- és nagykereskedelem) keresztül értékesíti. A kérdés az, hogy egy új könyv példányait hogyan allokálja az értékesítési láncban. Feltételezzük, hogy a kereslet ismert, Poisson-eloszlású. A készletezés költségeit szintén ismertnek tételezzük fel. Cél a költségek minimalizálása. = The aim of the paper is to analyze a practical real world problem. A publishing house is given. The publishing firm has contacts to a number of wholesaler / retailer enterprises and direct contact to customers to satisfy the market demand. The book publishers work in a project industry. The publisher faces with the problem to allocate the stocks of a given, newly published book to the wholesaler and retailer, and to hold some copies to satisfy the customers direct from the publisher. The distribution of the demand is unknown, but it can be estimated. The costs consist of inventory holding and shortage, backorder costs. The decision maker wants to minimize these relevant costs. The problem can be modeled as a one-warehouse and N-retailer supply chain with not identical demand distribution. The problem structure is similar that of a newsvendor model. It is assumed that the demand distribution follows a Poisson distribution.

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Crash reduction factors (CRFs) are used to estimate the potential number of traffic crashes expected to be prevented from investment in safety improvement projects. The method used to develop CRFs in Florida has been based on the commonly used before-and-after approach. This approach suffers from a widely recognized problem known as regression-to-the-mean (RTM). The Empirical Bayes (EB) method has been introduced as a means to addressing the RTM problem. This method requires the information from both the treatment and reference sites in order to predict the expected number of crashes had the safety improvement projects at the treatment sites not been implemented. The information from the reference sites is estimated from a safety performance function (SPF), which is a mathematical relationship that links crashes to traffic exposure. The objective of this dissertation was to develop the SPFs for different functional classes of the Florida State Highway System. Crash data from years 2001 through 2003 along with traffic and geometric data were used in the SPF model development. SPFs for both rural and urban roadway categories were developed. The modeling data used were based on one-mile segments that contain homogeneous traffic and geometric conditions within each segment. Segments involving intersections were excluded. The scatter plots of data show that the relationships between crashes and traffic exposure are nonlinear, that crashes increase with traffic exposure in an increasing rate. Four regression models, namely, Poisson (PRM), Negative Binomial (NBRM), zero-inflated Poisson (ZIP), and zero-inflated Negative Binomial (ZINB), were fitted to the one-mile segment records for individual roadway categories. The best model was selected for each category based on a combination of the Likelihood Ratio test, the Vuong statistical test, and the Akaike's Information Criterion (AIC). The NBRM model was found to be appropriate for only one category and the ZINB model was found to be more appropriate for six other categories. The overall results show that the Negative Binomial distribution model generally provides a better fit for the data than the Poisson distribution model. In addition, the ZINB model was found to give the best fit when the count data exhibit excess zeros and over-dispersion for most of the roadway categories. While model validation shows that most data points fall within the 95% prediction intervals of the models developed, the Pearson goodness-of-fit measure does not show statistical significance. This is expected as traffic volume is only one of the many factors contributing to the overall crash experience, and that the SPFs are to be applied in conjunction with Accident Modification Factors (AMFs) to further account for the safety impacts of major geometric features before arriving at the final crash prediction. However, with improved traffic and crash data quality, the crash prediction power of SPF models may be further improved.

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Since the Morris worm was released in 1988, Internet worms continue to be one of top security threats. For example, the Conficker worm infected 9 to 15 million machines in early 2009 and shut down the service of some critical government and medical networks. Moreover, it constructed a massive peer-to-peer (P2P) botnet. Botnets are zombie networks controlled by attackers setting out coordinated attacks. In recent years, botnets have become the number one threat to the Internet. The objective of this research is to characterize spatial-temporal infection structures of Internet worms, and apply the observations to study P2P-based botnets formed by worm infection. First, we infer temporal characteristics of the Internet worm infection structure, i.e., the host infection time and the worm infection sequence, and thus pinpoint patient zero or initially infected hosts. Specifically, we apply statistical estimation techniques on Darknet observations. We show analytically and empirically that our proposed estimators can significantly improve the inference accuracy. Second, we reveal two key spatial characteristics of the Internet worm infection structure, i.e., the number of children and the generation of the underlying tree topology formed by worm infection. Specifically, we apply probabilistic modeling methods and a sequential growth model. We show analytically and empirically that the number of children has asymptotically a geometric distribution with parameter 0.5, and the generation follows closely a Poisson distribution. Finally, we evaluate bot detection strategies and effects of user defenses in P2P-based botnets formed by worm infection. Specifically, we apply the observations of the number of children and demonstrate analytically and empirically that targeted detection that focuses on the nodes with the largest number of children is an efficient way to expose bots. However, we also point out that future botnets may self-stop scanning to weaken targeted detection, without greatly slowing down the speed of worm infection. We then extend the worm spatial infection structure and show empirically that user defenses, e.g. , patching or cleaning, can significantly mitigate the robustness and the effectiveness of P2P-based botnets. To counterattack, we evaluate a simple measure by future botnets that enhances topology robustness through worm re-infection.

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Thèse numérisée par la Direction des bibliothèques de l'Université de Montréal.

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Thèse numérisée par la Direction des bibliothèques de l'Université de Montréal.

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The study of forest re activity, in its several aspects, is essencial to understand the phenomenon and to prevent environmental public catastrophes. In this context the analysis of monthly number of res along several years is one aspect to have into account in order to better comprehend this tematic. The goal of this work is to analyze the monthly number of forest res in the neighboring districts of Aveiro and Coimbra, Portugal, through dynamic factor models for bivariate count series. We use a bayesian approach, through MCMC methods, to estimate the model parameters as well as to estimate the common latent factor to both series.

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Ecological models written in a mathematical language L(M) or model language, with a given style or methodology can be considered as a text. It is possible to apply statistical linguistic laws and the experimental results demonstrate that the behaviour of a mathematical model is the same of any literary text of any natural language. A text has the following characteristics: (a) the variables, its transformed functions and parameters are the lexic units or LUN of ecological models; (b) the syllables are constituted by a LUN, or a chain of them, separated by operating or ordering LUNs; (c) the flow equations are words; and (d) the distribution of words (LUM and CLUN) according to their lengths is based on a Poisson distribution, the Chebanov's law. It is founded on Vakar's formula, that is calculated likewise the linguistic entropy for L(M). We will apply these ideas over practical examples using MARIOLA model. In this paper it will be studied the problem of the lengths of the simple lexic units composed lexic units and words of text models, expressing these lengths in number of the primitive symbols, and syllables. The use of these linguistic laws renders it possible to indicate the degree of information given by an ecological model.