n.希腊字母表的第3个字母

原形：gamma复数：gammas

The gamma function, denoted by Γ(x), extends the concept of factorial to non-integer values; for example, Γ(5) = 4!.
(伽马函数，用Γ(x)表示，将阶乘的概念扩展到非整数值，例如Γ(5)等于4！
In probability theory, the gamma distribution is used to model continuous random variables with positive skewness; its shape parameter is often denoted as γ.
(在概率论中，伽马分布用于模拟具有正偏斜的连续随机变量；其形状参数通常表示为γ。
The gamma distribution has applications in physics, such as modeling the time between events in Poisson processes; γ is the rate parameter in this context.
伽马分布在物理学中有应用，如在泊松过程中的事件间时间模型，其中γ是速率参数。
In computer graphics, gamma correction is a technique used to adjust the brightness and contrast of images displayed on screens, where a value of 2.2 is commonly used for sRGB displays.
在计算机图形学中，伽马校正是一种调整屏幕上显示图像亮度和对比度的技术，通常sRGB显示器使用2.2作为伽马值。
In finance, the Black-Scholes model uses the gamma hedging strategy to manage the risk associated with options trading, where gamma represents the rate of change of delta with respect to the underlying asset's price.
在金融中，Black-Scholes模型利用伽马对冲策略来管理期权交易相关风险，其中伽马表示标的资产价格变化时delta的变化率。
The gamma exposure of a portfolio measures the sensitivity to changes in the volatility of assets, which is an important consideration for risk management.
投资组合的伽马暴露衡量的是对资产波动性的敏感性，这是风险管理中的一个重要因素。
In statistics, the gamma distribution is a conjugate prior for the precision parameter in a normal-inverse-gamma prior distribution, which simplifies Bayesian updating.
在统计学中，伽马分布是正态逆伽马先验分布中精确度参数的共轭先验，简化了贝叶斯更新过程。
The gamma distribution is related to the Weibull distribution, another widely-used continuous probability distribution, by a simple transformation of the parameters.
伽马分布与另一个广泛使用的连续概率分布——威布尔分布，通过参数的简单变换相关联。
In machine learning, the gamma distribution is sometimes used in the initialization of neural network weights, particularly in deep learning models.
在机器学习中，有时会使用伽马分布初始化神经网络权重，特别是在深度学习模型中。
The gamma function has a factorial-like behavior near the integers, but diverges to infinity as x approaches zero; this property is crucial in understanding its role in calculus.
伽马函数在接近整数时表现出类似于阶乘的行为，但当x趋向于零时发散到无穷大；这一特性对于理解它在微积分中的作用至关重要。