Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data01:16

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics.
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance, comparing...
Statistical Analysis: Overview01:11

Statistical Analysis: Overview

When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
Introduction to Nonparametric Statistics01:28

Introduction to Nonparametric Statistics

Nonparametric statistics offer a powerful alternative to traditional parametric methods, useful when assumptions about the population distribution cannot be made. Unlike parametric tests, which require data to follow a specific distribution with well-defined parameters (such as the mean and standard deviation), nonparametric tests do not require such constraints. This makes them particularly valuable when dealing with small sample sizes, skewed data, or ordinal and categorical variables.
One of...
Biostatistics: Overview01:20

Biostatistics: Overview

Biostatistics plays a crucial role in understanding and analyzing data in healthcare and biology. Biostatisticians conduct experiments, gather evidence, and draw meaningful conclusions using statistical methods and techniques. Different variables form the foundation of biostatistical analysis, allowing researchers to understand and interpret data effectively. These variables are classified into different types, each serving a specific purpose in statistical analysis.
Discrete variables are...
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

DNA transfer when using gloves in burglary simulations.

Forensic science international. Genetics·2022
Same author

Corrigendum to "A probabilistic approach to evaluate salivary microbiome in forensic science when the defense says: 'It is my twin brother'" [Forensic Sci. Int. Genet. 57, 102638].

Forensic science international. Genetics·2022
Same author

A probabilistic approach to evaluate salivary microbiome in forensic science when the Defense says: `It is my twin brother'.

Forensic science international. Genetics·2021
Same author

Voiding cystourethrography and <sup>99M</sup>TC-MAG3 renal scintigraphy in pediatric vesicoureteral reflux: what is the role of indirect cystography?

Journal of pediatric urology·2019
Same author

More on the question 'When does absence of evidence constitute evidence of absence?' How Bayesian confirmation theory can logically support the answer.

Forensic science international·2019
Same author

Critical analysis of forensic cut-offs and legal thresholds: A coherent approach to inference and decision.

Forensic science international·2018
Same journal

Technical note: Development of a UHPLC-MS/MS method for the analysis of hCG and IGF-I from dried blood spots: A preliminary study.

Forensic science international·2026
Same journal

A novel and robust deep learning model for sibling firearm matching.

Forensic science international·2026
Same journal

Changes in C-reactive protein levels over time in high-temperature environments using postmortem blood.

Forensic science international·2026
Same journal

Insights from the first synthetic cannabinoid clandestine lab dismantled in Brazil.

Forensic science international·2026
Same journal

Determination of the new psychoactive substances MDMB-4en-PINACA, ADB-BUTINACA and some of their metabolites in blood and urine using DLLE-LC-MS/MS: application to real forensic case samples.

Forensic science international·2026
Same journal

The revolver halo as a forensic marker: Raman spectroscopic evidence of primer-driven gunshot residue deposition.

Forensic science international·2026
See all related articles

Related Experiment Video

Updated: Jun 19, 2026

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
08:05

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Published on: June 30, 2020

Implementing statistical learning methods through Bayesian networks. Part 1: a guide to Bayesian parameter estimation

A Biedermann1, F Taroni, S Bozza

  • 1The University of Lausanne, Ecole des Sciences Criminelles, Institut de Police, Scientifique, le Batochime, 1015 Lausanne-Dorigny, Switzerland. alex.biedermann@unil.ch

Forensic Science International
|October 17, 2009
PubMed
Summary
This summary is machine-generated.

Bayesian networks offer a powerful framework for analyzing forensic science data and uncertainties. This paper introduces basic network fragments for building models to evaluate scientific evidence.

Related Experiment Videos

Last Updated: Jun 19, 2026

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
08:05

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Published on: June 30, 2020

Area of Science:

  • Forensic Science
  • Probability Theory
  • Graph Theory

Background:

  • Bayesian networks are increasingly used in forensic science for evaluating evidence.
  • Statistical procedures for uncertainty estimation are common but can be complex.
  • A need exists for accessible methods to apply these procedures to forensic data.

Purpose of the Study:

  • To provide a non-technical introduction to using Bayesian networks for forensic data analysis.
  • To present basic, context-independent Bayesian network fragments as building blocks.
  • To facilitate the illustration and implementation of statistical uncertainty procedures.

Main Methods:

  • Utilizing Bayesian networks as a framework for statistical analysis.
  • Focusing on the structure and rationale of fundamental network fragments.
  • Demonstrating the application of these fragments in constructing larger inference models.

Main Results:

  • A foundational understanding of Bayesian network components for forensic applications.
  • Reusable network fragments applicable to various forensic data processing tasks.
  • A clear pathway for readers to implement Bayesian network approaches.

Conclusions:

  • Bayesian networks provide an effective tool for enhancing the evaluation of scientific evidence in forensics.
  • The proposed network fragments simplify the construction of complex models for uncertainty analysis.
  • This work serves as a basis for applying Bayesian networks in specific forensic domains, such as document examination.