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Related Concept Videos

Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this particular...
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...
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
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Uncertainty: Overview

In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.

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Updated: May 18, 2026

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

The humble Bayesian: model checking from a fully Bayesian perspective.

Richard D Morey1, Jan-Willem Romeijn, Jeffrey N Rouder

  • 1University of Groningen, The Netherlands. r.d.morey@rug.nl

The British Journal of Mathematical and Statistical Psychology
|September 29, 2012
PubMed
Summary
This summary is machine-generated.

This study critiques the standard Bayesian statistics narrative, proposing a humble Bayesian approach. It emphasizes Bayesian confirmation theory alongside critical model checking for robust real-world statistical inferences.

Related Experiment Videos

Last Updated: May 18, 2026

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

Area of Science:

  • Statistics
  • Bayesian Inference
  • Philosophy of Science

Background:

  • The 'usual story' in Bayesian statistics focuses solely on model distributions for inference, neglecting model checking and revision.
  • This traditional approach is criticized for being inductivist rather than deductivist.
  • Gelman and Shalizi proposed a hypothetico-deductive approach to address these limitations.

Purpose of the Study:

  • To critique the limitations of the 'usual story' in Bayesian statistics.
  • To propose an alternative 'humble Bayesian' approach.
  • To re-center Bayesian confirmation theory as a core inferential method.

Main Methods:

  • Critique of the 'usual story' in Bayesian statistics as presented by Gelman and Shalizi.
  • Advocacy for a 'humble Bayesian' framework.
  • Emphasis on integrating Bayesian confirmation theory with model checking and critical assessment.

Main Results:

  • Agreement with Gelman and Shalizi's critique of the traditional Bayesian inference model.
  • Disagreement on abandoning Bayesian confirmation theory.
  • Proposal of a humble Bayesian approach that integrates confirmation theory with rigorous model evaluation.

Conclusions:

  • The 'usual story' in Bayesian statistics is insufficient due to its neglect of model checking and deductivist reasoning.
  • Bayesian confirmation theory remains a valuable inferential tool when used within a humble Bayesian framework.
  • A humble Bayesian approach enhances statistical inference by critically assessing models and their real-world applicability.