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Probability Histograms01:17

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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.
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Difference from Background: Limit of Detection01:05

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The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
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Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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Steps in Outbreak Investigation01:18

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In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
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Probability in Statistics01:14

Probability in Statistics

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Probability is the likelihood of an event occurring. The term event is defined as a collection of results of a procedure. An event is a simple event when an outcome cannot be divided into simpler parts.
An example of a simple event is a coin toss. The result of a coin toss is either a head or a tail. Here, head and tail are two simple events. These two simple events make up the sample space. Further, the probability of an event occurring falls within the range of 0 to 1. The probability of an...
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Prediction Intervals01:03

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The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
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Updated: Sep 5, 2025

Trajectory Data Analyses for Pedestrian Space-time Activity Study
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A machine learning method for estimating the probability of presence using presence-background data.

Yan Wang1, Chathuri L Samarasekara1, Lewi Stone1

  • 1School of Science RMIT University Melbourne Victoria Australia.

Ecology and Evolution
|July 5, 2022
PubMed
Summary

This study introduces a novel method for species distribution modeling, improving probability estimation from presence-background data. The new approach, using local knowledge, offers more robust results than the controversial Lele and Keim method.

Keywords:
RSPFconstrained LK methodlocal certaintylocal knowledgepresence‐backgroundprevalenceprobability of presence

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Area of Science:

  • Ecology
  • Computational Biology
  • Statistical Modeling

Background:

  • Estimating species presence probability from presence-background data is challenging.
  • Existing methods, like the Lele and Keim (LK) method, face controversy and limitations.

Purpose of the Study:

  • To develop a new, robust method for estimating species presence probability using presence-background data.
  • To evaluate the performance of the Lele and Keim method and its underlying RSPF condition.
  • To introduce and validate the 'local knowledge' condition as an alternative to strict population prevalence assumptions.

Main Methods:

  • Combined statistical and machine learning algorithms.
  • Re-evaluated the Lele and Keim (LK) method and its RSPF assumptions.
  • Developed and simulated a new method based on the 'local knowledge' condition.

Main Results:

  • The LK method with RSPF assumptions yields fragile probability estimations.
  • The proposed method utilizing local knowledge successfully estimates presence probability.
  • Local knowledge assumption proves effective for identifying absolute presence probability.

Conclusions:

  • The new method offers a more reliable approach to species distribution modeling.
  • The local knowledge condition provides a viable alternative for estimating absolute presence probability without absence data.
  • This work has significant implications for ecological modeling and biodiversity assessments.