Jove
Visualize
Contact Us

Related Concept Videos

Probability Distributions01:32

Probability Distributions

6.8K
 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...
6.8K
Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

4.0K
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...
4.0K
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

64
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.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
64
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

378
Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
378
Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data01:16

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

117
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,...
117
Binomial Probability Distribution01:15

Binomial Probability Distribution

10.2K
A binomial distribution is a probability distribution for a procedure with a fixed number of trials, where each trial can have only two outcomes.
The outcomes of a binomial experiment fit a binomial probability distribution. A statistical experiment can be classified as a binomial experiment if the following conditions are met:
There are a fixed number of trials. Think of trials as repetitions of an experiment. The letter n denotes the number of trials.
There are only two possible outcomes,...
10.2K

You might also read

Related Articles

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

Sort by
Same author

Top-down perceptual inference shaping the activity of early visual cortex.

Nature communications·2025
Same author

Identifying Transfer Learning in the Reshaping of Inductive Biases.

Open mind : discoveries in cognitive science·2024
Same author

Shifts in attention drive context-dependent subspace encoding in anterior cingulate cortex in mice during decision making.

Nature communications·2024
Same author

Continuous multiplexed population representations of task context in the mouse primary visual cortex.

Nature communications·2023
Same author

Sampling motion trajectories during hippocampal theta sequences.

eLife·2022
Same author

Tracking the contribution of inductive bias to individualised internal models.

PLoS computational biology·2022
Same journal

Institutions as cached computation for resource-rational negotiation.

The Behavioral and brain sciences·2026
Same journal

Delegation to legitimate authority as a resource rational mechanism.

The Behavioral and brain sciences·2026
Same journal

Moral cognition is contractualist, but does not work by simulating a bargaining process.

The Behavioral and brain sciences·2026
Same journal

Contractarian partiality, contractualist impartiality, and the question of falsifiability.

The Behavioral and brain sciences·2026
Same journal

The veil and the deal: Bargaining between case-specific solutions and unknown rules.

The Behavioral and brain sciences·2026
Same journal

Moral decision-making entails negotiation over the psychological mechanisms underlying decisions.

The Behavioral and brain sciences·2026
See all related articles
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 Experiment Video

Updated: Jun 12, 2025

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.0K

Bayes beyond the predictive distribution.

Anna Székely1,2, Gergő Orbán1

  • 1Department of Computational Sciences, HUN-REN Wigner Research Centre for Physics, Budapest, Hungary szekely.anna@wigner.hu orban.gergo@wigner.mta.huhttp://golab.wigner.mta.hu/people/anna-szekely/http://golab.wigner.mta.hu/people/gergo-orban/.

The Behavioral and Brain Sciences
|September 23, 2024
PubMed
Summary
This summary is machine-generated.

Meta-learned models offer a new paradigm for studying human cognition, potentially replacing Bayesian models. This commentary explores advantages beyond predictive distributions for evaluating these cognitive modeling paradigms.

More Related Videos

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

11.3K
Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM
12:26

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM

Published on: October 11, 2016

13.3K

Related Experiment Videos

Last Updated: Jun 12, 2025

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.0K
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

11.3K
Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM
12:26

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM

Published on: October 11, 2016

13.3K

Area of Science:

  • Cognitive Science
  • Computational Neuroscience
  • Artificial Intelligence

Background:

  • Meta-learned models are proposed as a new paradigm for studying human cognition.
  • These models are presented as alternatives to traditional Bayesian models.
  • A key feature is their capability to learn identical posterior predictive distributions.

Purpose of the Study:

  • To offer new perspectives for evaluating meta-learned versus Bayesian models.
  • To extend the comparison beyond predictive distribution capabilities.
  • To highlight arguments for the advantages of meta-learned modeling paradigms.

Main Methods:

  • Comparative analysis of meta-learned and Bayesian cognitive models.
  • Focus on theoretical arguments and conceptual frameworks.
  • Evaluation of modeling paradigms beyond predictive accuracy.

Main Results:

  • Identified limitations in comparing models solely on predictive distributions.
  • Proposed a broader framework for evaluating cognitive modeling approaches.
  • Highlighted unique advantages of meta-learned models in specific contexts.

Conclusions:

  • Meta-learned models present a promising new direction for cognitive science research.
  • A comprehensive evaluation requires considering factors beyond predictive distributions.
  • Further research is needed to fully elucidate the potential of meta-learning in understanding human cognition.