This function computes lower and upper posterior probabilities under an imprecise Beta model when prior information is not available.

An imprecise inference presented in the study of Walley (1996) <doi:10.1111/j.2517-6161.1996.tb02065.x> is one of the statistical reasoning methods when prior information is unavailable. Functions and utils needed for illustrating this inferential paradigm are implemented for classroom teaching and further comprehensive research. Two imprecise models are demonstrated using multinomial data and 2x2 contingency table data. The concepts of prior ignorance and imprecision are discussed in lower and upper probabilities. Representation invariance principle, hypothesis testing, decision-making, and further generalization are also illustrated.

Chel Hee Lee

imprecise101

Introduction to Imprecise Probabilities

Mikelis Bickis

Angela McCourt

ibm function

<dl><dt>n</dt>
<dd>total of trials</dd>
<dt>m</dt>
<dd>number of observations realized</dd>
<dt>s0</dt>
<dd>learning parameter</dd>
<dt>showplot</dt>
<dd>logical, TRUE by default</dd>
<dt>xlab1</dt>
<dd>x axis text</dd>
<dt>main1</dt>
<dd>main title text</dd></dl>

Arguments

Impreicse Beta Model — ibm

<dl>

<dt>n</dt>
<dd>total of trials</dd>


<dt>m</dt>
<dd>number of observations realized</dd>


<dt>s0</dt>
<dd>learning parameter</dd>


<dt>showplot</dt>
<dd>logical, TRUE by default</dd>


<dt>xlab1</dt>
<dd>x axis text</dd>


<dt>main1</dt>
<dd>main title text</dd>

</dl>

ibm: Impreicse Beta Model

Description

Usage

Value

Arguments

References

Examples