DistributionParameterAssumptions[dist]
给出符号式分布 dist 中的参数的假定所用的逻辑表达式.
DistributionParameterAssumptions
DistributionParameterAssumptions[dist]
给出符号式分布 dist 中的参数的假定所用的逻辑表达式.
更多信息
- DistributionParameterAssumptions 返回方程、不等式和域描述的逻辑组合,用于 dist 的参数假定.
- DistributionParameterAssumptions 可以对指定为符号式分布的任意单变量或者多变量分布生成参数假定.
范例
打开所有单元 关闭所有单元基本范例 (1)
范围 (4)
dists = Table[GammaDistribution[a, b], {a, {α, 1}}, {b, {β, 2}}]//FlattenTable[{d, DistributionParameterAssumptions[d]}, {d, dists}]//TableFormvalidDistributions = {GeometricDistribution[p], ParetoDistribution[k, a], BinormalDistribution[ρ], MultivariatePoissonDistribution[μ, {Subscript[μ, 1], Subscript[μ, 2]}]};Table[{dist, DistributionParameterAssumptions[dist]}, {dist, validDistributions}]//TableFormtdist = TransformedDistribution[x ^ 3, xPoissonDistribution[μ]];DistributionParameterAssumptions[tdist]ddist = EmpiricalDistribution[Range[10]];DistributionParameterAssumptions[ddist]应用 (4)
pdf = PDF[ExtremeValueDistribution[α, β], x]Integrate[x * pdf, {x, -∞, ∞}]Assuming[DistributionParameterAssumptions[ExtremeValueDistribution[α, β]], Simplify[%]]通过直接对 Integrate 给出假定,计算结果:
Integrate[x * pdf, {x, -∞, ∞}, Assumptions -> DistributionParameterAssumptions[ExtremeValueDistribution[α, β]]]Mean[ExtremeValueDistribution[α, β]]dist1 = ChiSquareDistribution[n];
dist2 = ChiSquareDistribution[m];
assumpts = DistributionParameterAssumptions[dist1] && DistributionParameterAssumptions[dist2];通过与设定参数的卷积计算服从 χ2 分布的变量的和的 pdf:
Convolve[PDF[dist1, t], PDF[dist2, t], t, x, Assumptions -> assumpts]% == PDF[ChiSquareDistribution[m + n], x]//Simplifydist = ProbabilityDistribution[(4 ^ (-1 + k)p((1 - p) * p) ^ (-1 + k)(-(3 / 2) + k)!) / (Sqrt[Pi]k!), {k, 1, Infinity, 1}, Assumptions -> 1 / 2 < p < 1];Sum[PDF[dist, k], {k, 1, Infinity}]FullSimplify[%, Not[DistributionParameterAssumptions[dist]]]对于设定的值
,对 Sum 给出假设值以验证总的概率为 1:
Sum[PDF[dist, k], {k, 1, Infinity}, Assumptions -> DistributionParameterAssumptions[dist]]获取第一类 Pearson 分布的设定,其中含有未知参数 b1 和 b0:
assumpts = DistributionParameterAssumptions[PearsonDistribution[1, 3, -2, 3, Subscript[b, 1], Subscript[b, 0]]]max = ArgMax[{Subscript[b, 1], assumpts}, Subscript[b, 1]]Plot[max, {Subscript[b, 0], -10, 10}, Filling -> -20, AxesLabel -> {Subscript[b, 0], Subscript[b, 1]}]属性和关系 (1)
DistributionParameterAssumptions 返回参数上的条件:
DistributionParameterAssumptions[BinomialDistribution[n, p]]DistributionParameterQ 假定参数是有效的:
DistributionParameterQ[BinomialDistribution[n, p]]DistributionParameterAssumptions[BinomialDistribution[20, 1 / 3]]DistributionParameterQ[BinomialDistribution[20, 1 / 3]]相关指南
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- 统计分布函数
文本
Wolfram Research (2010),DistributionParameterAssumptions,Wolfram 语言函数,https://reference.wolfram.com/language/ref/DistributionParameterAssumptions.html.
CMS
Wolfram 语言. 2010. "DistributionParameterAssumptions." Wolfram 语言与系统参考资料中心. Wolfram Research. https://reference.wolfram.com/language/ref/DistributionParameterAssumptions.html.
APA
Wolfram 语言. (2010). DistributionParameterAssumptions. Wolfram 语言与系统参考资料中心. 追溯自 https://reference.wolfram.com/language/ref/DistributionParameterAssumptions.html 年
BibTeX
@misc{reference.wolfram_2026_distributionparameterassumptions, author="Wolfram Research", title="{DistributionParameterAssumptions}", year="2010", howpublished="\url{https://reference.wolfram.com/language/ref/DistributionParameterAssumptions.html}", note=[Accessed: 08-September-2026]}
BibLaTeX
@online{reference.wolfram_2026_distributionparameterassumptions, organization={Wolfram Research}, title={DistributionParameterAssumptions}, year={2010}, url={https://reference.wolfram.com/language/ref/DistributionParameterAssumptions.html}, note=[Accessed: 08-September-2026]}