Bayesian estimation | Classification algorithms | Statistical classification

Averaged one-dependence estimators

Averaged one-dependence estimators (AODE) is a probabilistic classification learning technique. It was developed to address the attribute-independence problem of the popular naive Bayes classifier. It frequently develops substantially more accurate classifiers than naive Bayes at the cost of a modest increase in the amount of computation. (Wikipedia).

Video thumbnail

Covariance (9 of 17) What is the Correlation Coefficient?

Visit http://ilectureonline.com for more math and science lectures! To donate:a http://www.ilectureonline.com/donate https://www.patreon.com/user?u=3236071 We will calculate the correlation coefficients of 2 separate 2 data sets and graph the 2 graphs and see how the graphs corresponds t

From playlist COVARIANCE AND VARIANCE

Video thumbnail

Ex 2: Find a Data Value Given a Probability Involving a Normal Distribution - TI84

This video explains how to determine a data value in a normal distribution given a probability using a TI84. http://mathispower4u.com

From playlist The Normal Distribution

Video thumbnail

Ex 1: Average Value of a Function

This video provides an example of how to determine the average value of a function on an interval. Search Video Library at www.mathispower4u.wordpress.com

From playlist Applications of Definite Integration

Video thumbnail

Inverse normal with Z Table

Determining values of a variable at a particular percentile in a normal distribution

From playlist Unit 2: Normal Distributions

Video thumbnail

One Variable Statistics Using a Free Online App (MOER/MathAS)

This video explains how to use a free online app similar to the TI84 to determine one variable statistics. https://oervm.s3-us-west-2.amazonaws.com/tvm/indexStats.html

From playlist Statistics: Describing Data

Video thumbnail

TI84 One Variable Statistics Overview

This video explains the statistics given from the One Var Stats tool on the TI84. The method for determining the quartiles is discussed. http://mathispower4u.com

From playlist Statistics: Describing Data

Video thumbnail

How to Determine if Functions are Linearly Independent or Dependent using the Definition

How to Determine if Functions are Linearly Independent or Dependent using the Definition If you enjoyed this video please consider liking, sharing, and subscribing. You can also help support my channel by becoming a member https://www.youtube.com/channel/UCr7lmzIk63PZnBw3bezl-Mg/join Th

From playlist Zill DE 4.1 Preliminary Theory - Linear Equations

Video thumbnail

Normal Distribution: Mean, Median, Mode, and Standard Deviation From Graph

The video explains how to determine the mean, median, mode and standard deviation from a graph of a normal distribution.

From playlist The Normal Distribution

Video thumbnail

TI84 One Variable Statistics Overview with Frequency Table

This video explains the statistics given from the One Var Stats tool on the TI84. The data is given in a frequency table. The method for determining the quartiles is discussed. http://mathispower4u.com

From playlist Statistics: Describing Data

Video thumbnail

Alexandre Boritchev: Adding small viscosity to hyperbolic (stochastic) conservation laws

The mechanism responsible for blow-up is well-understood for many hyperbolic conservation laws. Indeed, for a whole class of problems including the Burgers equation and many aggregation-diffusion equations such as the 1D parabolic-elliptic Keller-Segel system, the time and nature

From playlist Partial Differential Equations

Video thumbnail

How Close are Shell Models to the 3D Navier–Stokes Equations?  by Dario Vincenzi

Program Turbulence: Problems at the Interface of Mathematics and Physics (ONLINE) ORGANIZERS: Uriel Frisch (Observatoire de la Côte d'Azur and CNRS, France), Konstantin Khanin (University of Toronto, Canada) and Rahul Pandit (Indian Institute of Science, Bengaluru) DATE: 07 December 202

From playlist Turbulence: Problems at The Interface of Mathematics and Physics (Online)

Video thumbnail

Lec 12 | MIT 2.830J Control of Manufacturing Processes, S08

Lecture 12: Full factorial models Instructor: Duane Boning, David Hardt View the complete course at: http://ocw.mit.edu/2-830JS08 License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu

From playlist MIT 2.830J, Control of Manufacturing Processes S08

Video thumbnail

Lecture 16 - Spectral Analysis

This is Lecture 16 of the COMP510 (Computational Finance) course taught by Professor Steven Skiena [http://www.cs.sunysb.edu/~skiena/] at Hong Kong University of Science and Technology in 2008. The lecture slides are available at: http://www.algorithm.cs.sunysb.edu/computationalfinance/pd

From playlist COMP510 - Computational Finance - 2007 HKUST

Video thumbnail

Stanford CS234: Reinforcement Learning | Winter 2019 | Lecture 3 - Model-Free Policy Evaluation

For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/ai Professor Emma Brunskill, Stanford University https://stanford.io/3eJW8yT Professor Emma Brunskill Assistant Professor, Computer Science Stanford AI for Human

From playlist Stanford CS234: Reinforcement Learning | Winter 2019

Video thumbnail

Welch's Method: The Averaged Periodogram

http://AllSignalProcessing.com for more great signal-processing content: ad-free videos, concept/screenshot files, quizzes, MATLAB and data files. Poor variance properties of the periodogram motivate averaging methods for estimating the power spectrum. In Welch's method the data is partit

From playlist Estimation and Detection Theory

Video thumbnail

Statistical Rethinking - Lecture 17

Lecture 17 - Multilevel models (2) - Statistical Rethinking: A Bayesian Course with R Examples

From playlist Statistical Rethinking Winter 2015

Video thumbnail

QRM 7-2: TS for RM 2 (PACF, ARMA estimation and forecasting)

Welcome to Quantitative Risk Management (QRM). In the second part of Lesson 7, we first introduce the partial autocorrelogram (PACF) and see how we can combine it with the ACF to understand something more about AR, MA and ARMA processes. We then deal with the important problems of estima

From playlist Quantitative Risk Management

Video thumbnail

Predictive Modelling Techniques | Data Science With R Tutorial

🔥 Advanced Certificate Program In Data Science: https://www.simplilearn.com/pgp-data-science-certification-bootcamp-program?utm_campaign=PredictiveModeling-0gf5iLTbiQM&utm_medium=Descriptionff&utm_source=youtube 🔥 Data Science Bootcamp (US Only): https://www.simplilearn.com/data-science-bo

From playlist R Programming For Beginners [2022 Updated]

Video thumbnail

EstimatingRegressionCoefficients.3.CorrelationToEstimateSlope

This video is brought to you by the Quantitative Analysis Institute at Wellesley College. The material is best viewed as part of the online resources that organize the content and include questions for checking understanding: https://www.wellesley.edu/qai/onlineresources

From playlist Estimating Regression Coefficients

Related pages

Incremental learning | Weka (machine learning) | Cluster-weighted modeling | Naive Bayes classifier