Jove
Visualize
Contact Us

Related Concept Videos

Interpretation of Confidence Intervals01:19

Interpretation of Confidence Intervals

6.3K
A confidence interval is a better estimate of the population than a point estimate, as it uses a range of values from a sample instead of a single value.
Confidence intervals have confidence coefficients that are crucial for their interpretation. The most common confidence coefficients are 0.90, 0.95, and 0.99, which can be written as percentages–90%, 95%, and 99%, respectively.
Suppose a person calculates a confidence interval with a confidence coefficient of 0.95. In that case, they can...
6.3K
Confidence Coefficient01:24

Confidence Coefficient

7.8K
The confidence coefficient is also known as the confidence level or degree of confidence. It is the percent expression for the probability, 1-α, that the confidence interval contains the true population parameter assuming that the confidence interval is obtained after sufficient unbiased sampling; for example, if the CL = 90%, then in 90 out of 100 samples the interval estimate will enclose the true population parameter. Here α is the area under the curve, distributed equally under...
7.8K
Confidence Intervals01:21

Confidence Intervals

7.0K
An unbiased point estimate is often insufficient to predict a population estimate, such as population mean or population proportion. In this scenario, a confidence interval is used. A confidence interval is an estimate similar to a  sample proportion. However, unlike the point estimate which is a single value, the confidence interval  contains a range of values. These values have lower and upper limits, known as confidence limits, and can be designated as L1 and L2, respectively.
A...
7.0K
Binomial Probability Distribution01:15

Binomial Probability Distribution

11.3K
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,...
11.3K
Prediction Intervals01:03

Prediction Intervals

2.3K
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. 
2.3K
Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

4.3K
The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor...
4.3K

You might also read

Related Articles

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

Sort by
Same author

Drug repositioning by belief networks and ensemble method.

Biomedical physics & engineering express·2026
Same author

Bayesian Inference for Drug Discovery by High Negative Samples and Oversampling.

Bioinformatics and biology insights·2025
Same author

Label Transfer for Drug Disease Association in Three Meta-Paths.

Evolutionary bioinformatics online·2024
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: Aug 13, 2025

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments
13:00

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

Published on: January 23, 2017

10.0K

Classifying of VN-Index Bullishness by Bayesian Inference.

Nam Anh Dao1, Viet Bach Dao2

  • 1Faculty of Information Technology, Electric Power University, Hanoi, Vietnam.

Big Data
|January 20, 2023
PubMed
Summary

This study introduces a probabilistic method using Bayesian theorem to identify bear and bull markets from macroeconomic data, aiding investor decision-making and stock portfolio optimization with high accuracy.

Keywords:
Bayesian inferencebullishnessclassificationtime series

More Related Videos

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods
13:04

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods

Published on: September 19, 2012

12.2K
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

11.4K

Related Experiment Videos

Last Updated: Aug 13, 2025

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments
13:00

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

Published on: January 23, 2017

10.0K
Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods
13:04

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods

Published on: September 19, 2012

12.2K
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

11.4K

Area of Science:

  • Quantitative Finance
  • Computational Economics
  • Machine Learning

Background:

  • Stock market decision-making involves complex analysis of market variations, risk, and return.
  • Dynamic market circumstances require sophisticated methods for identifying trends like bear and bull markets.

Purpose of the Study:

  • To design a probabilistic approach for discovering bear and bull markets using macroeconomic variables.
  • To enhance investor decision-making processes in stock market trading.

Main Methods:

  • Utilizing macroeconomic variables and flexible time series for stock features (return, risk, moving averages).
  • Employing Bayesian theorem for inferring conditional dependencies among stock variables.
  • Applying a case study with VN-index stock symbols for validation.

Main Results:

  • Demonstrated a learning method based on Bayesian inference for stock market analysis.
  • Achieved significant accuracy rates across various stock symbol types in the case study.
  • Successfully illustrated a consistent stock portfolio optimization strategy.

Conclusions:

  • The proposed probabilistic method effectively identifies market trends.
  • Bayesian inference provides a robust framework for stock market analysis and prediction.
  • The method offers a valuable tool for informed investor decision-making and portfolio management.