Gaussian

Pi,Gaussian-method

Tau,Gaussian-method

forget

forget,Gaussian,numeric,numeric-method

isapprox

isapprox,Gaussian,Gaussian,numeric-method

+,Gaussian,Gaussian-method

-,Gaussian,Gaussian-method

*,Gaussian,Gaussian-method

/,Gaussian,Gaussian-method

==,Gaussian,Gaussian-method

performance,Player-method

Most estimators implemented by the video game industry cannot obtain reliable initial estimates nor guarantee comparability between distant estimates. TrueSkill Through Time solves all these problems by modeling the entire history of activities using a single Bayesian network allowing the information to propagate correctly throughout the system. This algorithm requires only a few iterations to converge, allowing millions of observations to be analyzed using any low-end computer.
Landfried G, Mocskos E (2025). "TrueSkill Through Time: Reliable Initial Skill Estimates and Historical Comparability with Julia, Python, and R." <doi:10.18637/jss.v112.i06>.
The core ideas implemented in this project were developed by Dangauthier P, Herbrich R, Minka T, Graepel T (2007). "Trueskill through time: Revisiting the history of chess.".

Gustavo Landfried

TrueSkillThroughTime

Skill Estimation Based on a Single Bayesian Network

Gaussian function

<dl><dt>mu</dt>
<dd>A number, the mean of the Gaussian distribution.</dd>
<dt>sigma</dt>
<dd>A number, the standar deviation of the Gaussian distribution.</dd>
<dt>N</dt>
<dd>A Gaussian object</dd>
<dt>gamma</dt>
<dd>The dynamic factor, the dynamic uncertainty</dd>
<dt>t</dt>
<dd>The elapsed time</dd>
<dt>M</dt>
<dd>A Gaussian object</dd>
<dt>tol</dt>
<dd>The tolerance threshold for comparitions</dd>
<dt>e1</dt>
<dd>A Gaussian object</dd>
<dt>e2</dt>
<dd>A Gaussian object</dd>
<dt>a</dt>
<dd>A Gaussian object</dd></dl>

Arguments

Gaussian — Gaussian

<dl>

<dt>mu</dt>
<dd>A number, the mean of the Gaussian distribution.</dd>


<dt>sigma</dt>
<dd>A number, the standar deviation of the Gaussian distribution.</dd>


<dt>N</dt>
<dd>A Gaussian object</dd>


<dt>gamma</dt>
<dd>The dynamic factor, the dynamic uncertainty</dd>


<dt>t</dt>
<dd>The elapsed time</dd>


<dt>M</dt>
<dd>A Gaussian object</dd>


<dt>tol</dt>
<dd>The tolerance threshold for comparitions</dd>


<dt>e1</dt>
<dd>A Gaussian object</dd>


<dt>e2</dt>
<dd>A Gaussian object</dd>


<dt>a</dt>
<dd>A Gaussian object</dd>

</dl>

Gaussian: Gaussian

Description

Usage

Value

Arguments

Examples