本文整理汇总了C#中Beta.GetMeanSquare方法的典型用法代码示例。如果您正苦于以下问题：C# Beta.GetMeanSquare方法的具体用法？C# Beta.GetMeanSquare怎么用？C# Beta.GetMeanSquare使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类Beta的用法示例。
在下文中一共展示了Beta.GetMeanSquare方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的C#代码示例。
示例1: ProbTrueAverageConditional
/// <summary>
/// EP message to 'probTrue'
/// </summary>
/// <param name="sample">Incoming message from 'sample'. Must be a proper distribution. If uniform, the result will be uniform.</param>
/// <param name="probTrue">Incoming message from 'probTrue'.</param>
/// <returns>The outgoing EP message to the 'probTrue' argument</returns>
/// <remarks><para>
/// The outgoing message is a distribution matching the moments of 'probTrue' as the random arguments are varied.
/// The formula is <c>proj[p(probTrue) sum_(sample) p(sample) factor(sample,probTrue)]/p(probTrue)</c>.
/// </para></remarks>
/// <exception cref="ImproperMessageException"><paramref name="sample"/> is not a proper distribution</exception>
public static Beta ProbTrueAverageConditional([SkipIfUniform] Bernoulli sample, Beta probTrue)
{
// this code is similar to DiscreteFromDirichletOp.PAverageConditional()
if (probTrue.IsPointMass) {
return Beta.Uniform();
}
if (sample.IsPointMass) {
// shortcut
return ProbTrueConditional(sample.Point);
}
if (!probTrue.IsProper()) throw new ImproperMessageException(probTrue);
// q(x) is the distribution stored in this.X.
// q(p) is the distribution stored in this.P.
// f(x,p) is the factor.
// Z = sum_x q(x) int_p f(x,p)*q(p) = q(false)*E[1-p] + q(true)*E[p]
// Ef[p] = 1/Z sum_x q(x) int_p p*f(x,p)*q(p) = 1/Z (q(false)*E[p(1-p)] + q(true)*E[p^2])
// Ef[p^2] = 1/Z sum_x q(x) int_p p^2*f(x,p)*q(p) = 1/Z (q(false)*E[p^2(1-p)] + q(true)*E[p^3])
// var_f(p) = Ef[p^2] - Ef[p]^2
double mo = probTrue.GetMean();
double m2o = probTrue.GetMeanSquare();
double pT = sample.GetProbTrue();
double pF = sample.GetProbFalse();
double Z = pF * (1 - mo) + pT * mo;
double m = pF * (mo - m2o) + pT * m2o;
m = m / Z;
if (!Beta.AllowImproperSum) {
if (pT < 0.5) {
double inc = probTrue.TotalCount * (mo / m - 1);
return new Beta(1, 1 + inc);
} else {
double inc = probTrue.TotalCount * ((1 - mo) / (1 - m) - 1);
return new Beta(1 + inc, 1);
}
} else {
double m3o = probTrue.GetMeanCube();
double m2 = pF * (m2o - m3o) + pT * m3o;
m2 = m2 / Z;
Beta result = Beta.FromMeanAndVariance(m, m2 - m * m);
result.SetToRatio(result, probTrue);
return result;
}
}