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Related Concept Videos

Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
Testing a Claim about Population Proportion01:24

Testing a Claim about Population Proportion

A complete procedure for testing a claim about a population proportion is provided here.
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...
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Choosing Between z and t Distribution

The z and the Student t distribution estimate the population mean using the sample mean and standard deviation. However, to decide which distribution to use for a calculation, one needs to determine the sample size, the nature of the distribution, and whether the population standard deviation is known. If the population standard deviation is known and the population is normally distributed, or if the sample size is greater than 30, the z distribution is preferred. The Student t distribution is...
Probability Laws01:49

Probability Laws

Overview
Probability Histograms01:17

Probability Histograms

A probability histogram is a visual representation of a probability distribution. Similar a typical histogram, the probability histogram consists of contiguous (adjoining) boxes. It has both a horizontal axis and a vertical axis. The horizontal axis is labeled with what the data represents. The vertical axis is labeled with probability. Each rectangular bar in the histogram is 1 unit wide, which suggests that the area under each bar equals the probability, P(x), where x is 1, 2, 3, and so on.
Probability Distributions01:32

Probability Distributions

The probability of a random variable x  is the likelihood of its occurrence. A probability distribution represents the probabilities of a random variable using a formula, graph, or table. There are two types of probability distribution– discrete probability distribution and continuous probability distribution.
A discrete probability distribution is a probability distribution of discrete random variables. It can be categorized into binomial probability distribution and Poisson probability...

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Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
08:03

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Published on: December 7, 2021

Alternative estimate of source distribution in microbial source tracking using posterior probabilities.

Joshua Greenberg1, Bertram Price, Adam Ware

  • 1Price Associates, Inc., One North Broadway Ste 406, White Plains, NY 10601, USA.

Water Research
|February 17, 2010
PubMed
Summary

Averaging microbial source probabilities improves estimates of fecal contamination sources in water. This new method offers more accurate watershed microbial source tracking (MST) than traditional classification approaches.

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Cost-effective Method for Microbial Source Tracking Using Specific Human and Animal Viruses
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Published on: December 3, 2011

Area of Science:

  • Environmental microbiology
  • Water quality assessment
  • Statistical modeling

Background:

  • Microbial source tracking (MST) identifies sources of fecal contamination in surface waters.
  • Current MST methods rely on classifying microbial isolates to estimate source contributions.
  • Improving the accuracy of MST is crucial for effective watershed management.

Purpose of the Study:

  • To propose and compare an alternative method for estimating microbial contamination source distributions.
  • To evaluate the accuracy of averaging posterior probabilities versus classification for MST.

Main Methods:

  • A Monte Carlo simulation was employed to compare two MST estimation methods.
  • The study simulated various watershed scenarios to test method robustness.
  • The methods compared were: classification of isolates and averaging posterior probabilities.

Main Results:

  • Averaging posterior probabilities across isolates resulted in more accurate source distribution estimates.
  • The proposed method demonstrated superior performance compared to the traditional classification approach.
  • Simulation results confirmed the enhanced accuracy of the probability averaging method.

Conclusions:

  • Averaging posterior probabilities is a more reliable approach for microbial source tracking.
  • This method enhances the accuracy of determining human and animal contributions to water contamination.
  • The findings support the adoption of probability averaging for improved watershed management strategies.