715 resultados para Probability Weight


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This note shows that, under appropriate conditions, preferences may be locally approximated by the linear utility or risk-neutral preference functional associated with a local probability transformation.

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This work provides a holistic investigation into the realm of feature modeling within software product lines. The work presented identifies limitations and challenges within the current feature modeling approaches. Those limitations include, but not limited to, the dearth of satisfactory cognitive presentation, inconveniency in scalable systems, inflexibility in adapting changes, nonexistence of predictability of models behavior, as well as the lack of probabilistic quantification of model’s implications and decision support for reasoning under uncertainty. The work in this thesis addresses these challenges by proposing a series of solutions. The first solution is the construction of a Bayesian Belief Feature Model, which is a novel modeling approach capable of quantifying the uncertainty measures in model parameters by a means of incorporating probabilistic modeling with a conventional modeling approach. The Bayesian Belief feature model presents a new enhanced feature modeling approach in terms of truth quantification and visual expressiveness. The second solution takes into consideration the unclear support for the reasoning under the uncertainty process, and the challenging constraint satisfaction problem in software product lines. This has been done through the development of a mathematical reasoner, which was designed to satisfy the model constraints by considering probability weight for all involved parameters and quantify the actual implications of the problem constraints. The developed Uncertain Constraint Satisfaction Problem approach has been tested and validated through a set of designated experiments. Profoundly stating, the main contributions of this thesis include the following: • Develop a framework for probabilistic graphical modeling to build the purported Bayesian belief feature model. • Extend the model to enhance visual expressiveness throughout the integration of colour degree variation; in which the colour varies with respect to the predefined probabilistic weights. • Enhance the constraints satisfaction problem by the uncertainty measuring of the parameters truth assumption. • Validate the developed approach against different experimental settings to determine its functionality and performance.

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Given the heterogeneity of effect sizes within the population for any treatment, identifying moderators of outcomes is critical [1]. In weight management programs, there is a high individual variability in terms of weight loss and an overall modest success [2]. Some people will adopt and sustain attitudes and behaviors associated with weight loss, while others won’t [3]. To predict weight loss outcome just from the subject’s baseline information would be very valuable [4,5]. It would allow to: - Better match between treatments and individuals - Identify the participants with less probability of success (or potential dropouts) in a given treatment and direct them to alternative therapies - Target limited resources to those most likely to succeed - Increase cost-effectiveness and improve success rates of the programs Few studies have been dedicated to describe baseline predictors of treatment success. The Healthy Weight for Life (USA) study is one of the few. Its findings are now being cross-validated in Portuguese samples. This paper describes these cross-cultural comparisons.

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Although extended secondary prophylaxis with low-molecular-weight heparin was recently shown to be more effective than warfarin for cancer-related venous thromboembolism, its cost-effectiveness compared to traditional prophylaxis with warfarin is uncertain. We built a decision analytic model to evaluate the clinical and economic outcomes of a 6-month course of low-molecular-weight heparin or warfarin therapy in 65-year-old patients with cancer-related venous thromboembolism. We used probability estimates and utilities reported in the literature and published cost data. Using a US societal perspective, we compared strategies based on quality-adjusted life-years (QALYs) and lifetime costs. The incremental cost-effectiveness ratio of low-molecular-weight heparin compared with warfarin was 149,865 dollars/QALY. Low-molecular-weight heparin yielded a quality-adjusted life expectancy of 1.097 QALYs at the cost of 15,329 dollars. Overall, 46% (7108 dollars) of the total costs associated with low-molecular-weight heparin were attributable to pharmacy costs. Although the low-molecular-weigh heparin strategy achieved a higher incremental quality-adjusted life expectancy than the warfarin strategy (difference of 0.051 QALYs), this clinical benefit was offset by a substantial cost increment of 7,609 dollars. Cost-effectiveness results were sensitive to variation of the early mortality risks associated with low-molecular-weight heparin and warfarin and the pharmacy costs for low-molecular-weight heparin. Based on the best available evidence, secondary prophylaxis with low-molecular-weight heparin is more effective than warfarin for cancer-related venous thromboembolism. However, because of the substantial pharmacy costs of extended low-molecular-weight heparin prophylaxis in the US, this treatment is relatively expensive compared with warfarin.

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An indirect estimate of consumable food and probability of acquiring food in a blowfly species, Chrysomya putoria, is presented. This alternative procedure combines three distinct models to estimate consumable food in the context of the exploitative competition experienced by immature individuals in blowfly populations. The relevant parameters are derived from data for pupal weight and survival and estimates of density-independent larval mortality in twenty different larval densities. As part of this procedure, the probability of acquiring food per unit of time and the time taken to exhaust the food supply are also calculated. The procedure employed here may be valuable for estimations in insects whose immature stages develop inside the food substrate, where it is difficult to partial out confounding effects such as separation of faeces. This procedure also has the advantage of taking into account the population dynamics of immatures living under crowded conditions, which are particularly characteristic of blowflies and other insects as well.

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BACKGROUND The effect of the macronutrient composition of the usual diet on long term weight maintenance remains controversial. METHODS 373,803 subjects aged 25-70 years were recruited in 10 European countries (1992-2000) in the PANACEA project of the EPIC cohort. Diet was assessed at baseline using country-specific validated questionnaires and weight and height were measured at baseline and self-reported at follow-up in most centers. The association between weight change after 5 years of follow-up and the iso-energetic replacement of 5% of energy from one macronutrient by 5% of energy from another macronutrient was assessed using multivariate linear mixed-models. The risk of becoming overweight or obese after 5 years was investigated using multivariate Poisson regressions stratified according to initial Body Mass Index. RESULTS A higher proportion of energy from fat at the expense of carbohydrates was not significantly associated with weight change after 5 years. However, a higher proportion of energy from protein at the expense of fat was positively associated with weight gain. A higher proportion of energy from protein at the expense of carbohydrates was also positively associated with weight gain, especially when carbohydrates were rich in fibre. The association between percentage of energy from protein and weight change was slightly stronger in overweight participants, former smokers, participants ≥60 years old, participants underreporting their energy intake and participants with a prudent dietary pattern. Compared to diets with no more than 14% of energy from protein, diets with more than 22% of energy from protein were associated with a 23-24% higher risk of becoming overweight or obese in normal weight and overweight subjects at baseline. CONCLUSION Our results show that participants consuming an amount of protein above the protein intake recommended by the American Diabetes Association may experience a higher risk of becoming overweight or obese during adult life.

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BACKGROUND Identifying individuals at high risk of excess weight gain may help targeting prevention efforts at those at risk of various metabolic diseases associated with weight gain. Our aim was to develop a risk score to identify these individuals and validate it in an external population. METHODS We used lifestyle and nutritional data from 53°758 individuals followed for a median of 5.4 years from six centers of the European Prospective Investigation into Cancer and Nutrition (EPIC) to develop a risk score to predict substantial weight gain (SWG) for the next 5 years (derivation sample). Assuming linear weight gain, SWG was defined as gaining ≥ 10% of baseline weight during follow-up. Proportional hazards models were used to identify significant predictors of SWG separately by EPIC center. Regression coefficients of predictors were pooled using random-effects meta-analysis. Pooled coefficients were used to assign weights to each predictor. The risk score was calculated as a linear combination of the predictors. External validity of the score was evaluated in nine other centers of the EPIC study (validation sample). RESULTS Our final model included age, sex, baseline weight, level of education, baseline smoking, sports activity, alcohol use, and intake of six food groups. The model's discriminatory ability measured by the area under a receiver operating characteristic curve was 0.64 (95% CI = 0.63-0.65) in the derivation sample and 0.57 (95% CI = 0.56-0.58) in the validation sample, with variation between centers. Positive and negative predictive values for the optimal cut-off value of ≥ 200 points were 9% and 96%, respectively. CONCLUSION The present risk score confidently excluded a large proportion of individuals from being at any appreciable risk to develop SWG within the next 5 years. Future studies, however, may attempt to further refine the positive prediction of the score.

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BACKGROUND There is a growing worldwide trend of obesity in children. Identifying the causes and modifiable factors associated with child obesity is important in order to design effective public health strategies.Our objective was to provide empirical evidence of the association that some individual and environmental factors may have with child excess weight. METHOD A cross-sectional study was performed using multi-stage probability sampling of 978 Spanish children aged between 8 and 17 years, with objectively measured height and weight, along with other individual, family and neighborhood variables. Crude and adjusted odds ratios were calculated. RESULTS In 2012, 4 in 10 children were either overweight or obese with a higher prevalence amongst males and in the 8-12 year age group. Child obesity was associated negatively with the socio-economic status of the adult responsible for the child's diet, OR 0.78 (CI95% 0.59-1.00), girls OR 0.75 (CI95% 0.57-0.99), older age of the child (0.41; CI95% 0.31-0.55), daily breakfast (OR 0.59; p = 0.028) and half an hour or more of physical activity every day. No association was found for neighborhood variables relating to perceived neighborhood quality and safety. CONCLUSION This study identifies potential modifiable factors such as physical activity, daily breakfast and caregiver education as areas for public health policies. To be successful, an intervention should take into account both individual and family factors when designing prevention strategies to combat the worldwide epidemic of child excess weight.

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Exchange matrices represent spatial weights as symmetric probability distributions on pairs of regions, whose margins yield regional weights, generally well-specified and known in most contexts. This contribution proposes a mechanism for constructing exchange matrices, derived from quite general symmetric proximity matrices, in such a way that the margin of the exchange matrix coincides with the regional weights. Exchange matrices generate in turn diffusive squared Euclidean dissimilarities, measuring spatial remoteness between pairs of regions. Unweighted and weighted spatial frameworks are reviewed and compared, regarding in particular their impact on permutation and normal tests of spatial autocorrelation. Applications include tests of spatial autocorrelation with diagonal weights, factorial visualization of the network of regions, multivariate generalizations of Moran's I, as well as "landscape clustering", aimed at creating regional aggregates both spatially contiguous and endowed with similar features.

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This paper presents a statistical model for the quantification of the weight of fingerprint evidence. Contrarily to previous models (generative and score-based models), our model proposes to estimate the probability distributions of spatial relationships, directions and types of minutiae observed on fingerprints for any given fingermark. Our model is relying on an AFIS algorithm provided by 3M Cogent and on a dataset of more than 4,000,000 fingerprints to represent a sample from a relevant population of potential sources. The performance of our model was tested using several hundreds of minutiae configurations observed on a set of 565 fingermarks. In particular, the effects of various sub-populations of fingers (i.e., finger number, finger general pattern) on the expected evidential value of our test configurations were investigated. The performance of our model indicates that the spatial relationship between minutiae carries more evidential weight than their type or direction. Our results also indicate that the AFIS component of our model directly enables us to assign weight to fingerprint evidence without the need for the additional layer of complex statistical modeling involved by the estimation of the probability distributions of fingerprint features. In fact, it seems that the AFIS component is more sensitive to the sub-population effects than the other components of the model. Overall, the data generated during this research project contributes to support the idea that fingerprint evidence is a valuable forensic tool for the identification of individuals.

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The objective of this study was to estimate genetic parameters for survival and weight of Nile tilapia (Oreochromis niloticus), farmed in cages and ponds in Brazil, and to predict genetic gain under different scenarios. Survival was recorded as a binary response (dead or alive), during harvest time in the 2008 grow-out period. Genetic parameters were estimated using a Bayesian mixed linear-threshold animal model via Gibbs sampling. The breeding population consisted of 2,912 individual fish, which were analyzed together with the pedigree of 5,394 fish. The heritabilities estimates, with 95% posterior credible intervals, for tagging weight, harvest weight and survival were 0.17 (0.09-0.27), 0.21 (0.12-0.32) and 0.32 (0.22-0.44), respectively. Credible intervals show a 95% probability that the true genetic correlations were in a favourable direction. The selection for weight has a positive impact on survival. Estimated genetic gain was high when selecting for harvest weight (5.07%), and indirect gain for tagging weight (2.17%) and survival (2.03%) were also considerable.

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A generalized or tunable-kernel model is proposed for probability density function estimation based on an orthogonal forward regression procedure. Each stage of the density estimation process determines a tunable kernel, namely, its center vector and diagonal covariance matrix, by minimizing a leave-one-out test criterion. The kernel mixing weights of the constructed sparse density estimate are finally updated using the multiplicative nonnegative quadratic programming algorithm to ensure the nonnegative and unity constraints, and this weight-updating process additionally has the desired ability to further reduce the model size. The proposed tunable-kernel model has advantages, in terms of model generalization capability and model sparsity, over the standard fixed-kernel model that restricts kernel centers to the training data points and employs a single common kernel variance for every kernel. On the other hand, it does not optimize all the model parameters together and thus avoids the problems of high-dimensional ill-conditioned nonlinear optimization associated with the conventional finite mixture model. Several examples are included to demonstrate the ability of the proposed novel tunable-kernel model to effectively construct a very compact density estimate accurately.

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Iso-score curves graph (iSCG) and mathematical relationships between Scoring Parameters (SP) and Forecasting Parameters (FP) can be used in Economic Scoring Formulas (ESF) used in tendering to distribute the score among bidders in the economic part of a proposal. Each contracting authority must set an ESF when publishing tender specifications and the strategy of each bidder will differ depending on the ESF selected and the weight of the overall proposal scoring. The various mathematical relationships and density distributions that describe the main SPs and FPs, and the representation of tendering data by means of iSCGs, enable the generation of two new types of graphs that can be very useful for bidders who want to be more competitive: the scoring and position probability graphs.

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The aim of the present study was to determine the classification error probabilities, as lean or obese, in hypercaloric diet-induced obesity, which depends on the variable used to characterize animal obesity. In addition, the misclassification probabilities in animals submitted to normocaloric diet were also evaluated. Male Wistar rats were randomly distributed into two groups: normal diet (ND; n=3 1; 3,5 Kcal/g) and hypercaloric diet (HD; n=31; 4,6 Kcal/g). The ND group received commercial Labina rat feed and HD animals a cycle of five hypercaloric diets for a 14-week period. The variables analysed were body weight, body composition, body weight to length ratio, Lee index, body mass index and misclassification probability A 5% significance level was used. The hypercaloric pellet-diet cycle promoted increase of body weight, carcass fat, body weight to length ratio and Lee index. The total misclassification probabilities ranged from 19.21 % to 40.91 %. In Conclusion, the results of this experiment show that rnisclassification probabilities Occur when dietary manipulation is used to promote obesity in animals. This misjudgement ranges from 19.49% to 40.52% in hypercaloric diet and 18.94% to 41.30% in normocaloric diet.

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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)