Rustacean Station

Kernel Density Estimation with Seaton Ullberg

Dec 9, 2022
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Episode notes
1
Introduction
00:00 • 2min
2
A Few Words About Probability Density
02:23 • 2min
3
Is Kernel Density Estimation a Good Modeling Technique?
04:46 • 2min
4
The Kernel Density Estimation Technique
07:11 • 2min
5
Using Kernel Density Estimation for Model Development?
09:08 • 2min
6
Using KDE to Predict the Output Target Property
10:44 • 2min
7
The KDE Technique Is Really Critical to Getting Accurate Estimates, Right?
13:04 • 3min
8
How Much Weight Should I Give to Katie Versus Like Other Competing Algorithms?
16:28 • 4min
9
Is It Necessary to Parameterize the KDE?
20:01 • 2min
10
KDE Performance - The Choice of Kernel Function
22:11 • 2min
11
Is It Possible to Fix the Bandwidth or Change the Kernel?
24:06 • 3min
12
Is There a Good or a Bad Dimension?
27:21 • 2min
13
Is There a Way That Katie Can Say, Okay, This Dimension Is Not Fit for This Kind of Model?
29:21 • 2min
14
The Evolution of KDE and the Evolution of Genetics
30:58 • 3min
15
Why Did You Attempt to Write Something Like This?
33:38 • 3min
16
Is There a Lin F.A. Linfa Library?
36:46 • 2min
17
The Twitter Account for Luca Palmeri
38:19 • 2min
18
Float 64
40:11 • 3min