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## Search Results: probability

## Mischievous Odds Ratiosdx.plos.org/10.1371/journal.pmed.0030205 | ||

## Correction: Gendist: An R Package for Generated Probability Distribution Modelsdx.plos.org/10.1371/journal.pone.0160903 | ||

## Crossing Over…Markov Meets Mendeldx.plos.org/10.1371/journal.pcbi.1002462 | ||

Chromosomal crossover is a biological mechanism to combine parental traits. It is perhaps the first mechanism ever taught in any introductory biology class. The formulation of crossover, and resulting recombination, came about 100 years after Mendel's famous experiments. To a great extent, this formulation is consistent with the basic genetic findings of Mendel. More importantly, it provides a mathematical insight for his two laws (and corrects them). From a mathematical persp... | ||

## Evaluation of Two Methods to Estimate and Monitor Bird Populationsdx.plos.org/10.1371/journal.pone.0003047 | ||

Background: Effective management depends upon accurately estimating trends in abundance of bird populations over time, and in some cases estimating abundance. Two population estimation methods, double observer (DO) and double sampling (DS), have been advocated for avian population studies and the relative merits and short-comings of these methods remain an area of debate. Methodology/Principal Findings: We used simulations to evaluate the performances of these two population estimation... | ||

## Correction: A Collision Probability Model of Portal Vein Tumor Thrombus Formation in Hepatocellular Carcinomadx.plos.org/10.1371/journal.pone.0138165 | ||

## Using Inverse Probability Bootstrap Sampling to Eliminate Sample Induced Bias in Model Based Analysis of Unequal Probability Samplesdx.plos.org/10.1371/journal.pone.0131765 | ||

In ecology, as in other research fields, efficient sampling for population estimation often drives sample designs toward unequal probability sampling, such as in stratified sampling. Design based statistical analysis tools are appropriate for seamless integration of sample design into the statistical analysis. However, it is also common and necessary, after a sampling design has been implemented, to use datasets to address questions that, in many cases, were not considered during the ... | ||

## Activity in Inferior Parietal and Medial Prefrontal Cortex Signals the Accumulation of Evidence in a Probability Learning Taskdx.plos.org/10.1371/journal.pcbi.1002895 | ||

In an uncertain environment, probabilities are key to predicting future events and making adaptive choices. However, little is known about how humans learn such probabilities and where and how they are encoded in the brain, especially when they concern more than two outcomes. During functional magnetic resonance imaging (fMRI), young adults learned the probabilities of uncertain stimuli through repetitive sampling. Stimuli represented payoffs and participants had to predict th... | ||

## The Probabilities of Unique Eventsdx.plos.org/10.1371/journal.pone.0045975 | ||

Many theorists argue that the probabilities of unique events, even real possibilities such as President Obama's re-election, are meaningless. As a consequence, psychologists have seldom investigated them. We propose a new theory (implemented in a computer program) in which such estimates depend on an intuitive non-numerical system capable only of simple procedures, and a deliberative system that maps intuitions into numbers. The theory predicts that estimates of the probabilit... | ||

## Correction: Inferring Tree Causal Models of Cancer Progression with Probability Raisingdx.plos.org/10.1371/journal.pone.0115570 | ||

## Specifying the Probability Characteristics of Funnel Plot Control Limits: An Investigation of Three Approachesdx.plos.org/10.1371/journal.pone.0045723 | ||

Background: Emphasis is increasingly being placed on the monitoring and comparison of clinical outcomes between healthcare providers. Funnel plots have become a standard graphical methodology to identify outliers and comprise plotting an outcome summary statistic from each provider against a specified ‘target’ together with upper and lower control limits. With discrete probability distributions it is not possible to specify the exact probability that an observation from an ‘in-co... | ||

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