Truncated normal distribution

Probability density function Probability density function for the truncated normal distribution for different sets of parameters. In all cases, a = −10 and b = 10. For the black: μ = −8, σ = 2; blue: μ = 0, σ = 2; red: μ = 9, σ = 10; orange: μ = 0, σ = 10.
Cumulative distribution function Cumulative distribution function for the truncated normal distribution for different sets of parameters. In all cases, a = −10 and b = 10. For the black: μ = −8, σ = 2; blue: μ = 0, σ = 2; red: μ = 9, σ = 10; orange: μ = 0, σ = 10.
Notation	${\displaystyle \xi ={\frac {x-\mu }{\sigma }},\ \alpha ={\frac {a-\mu }{\sigma }},\ \beta ={\frac {b-\mu }{\sigma }}}$ ${\displaystyle Z=\Phi (\beta )-\Phi (\alpha )}$
Parameters	${\displaystyle \mu \in \mathbb {R} }$ ${\displaystyle \sigma ^{2}\geq 0}$ (but see definition) ${\displaystyle a\in \mathbb {R} }$ — minimum value of ${\displaystyle x}$ ${\displaystyle b\in \mathbb {R} }$ — maximum value of ${\displaystyle x}$ (${\displaystyle b>a}$)
Support	${\displaystyle x\in [a,b]}$
PDF	${\displaystyle f(x;\mu ,\sigma ,a,b)={\frac {\varphi (\xi )}{\sigma Z}}\,}$
CDF	${\displaystyle F(x;\mu ,\sigma ,a,b)={\frac {\Phi (\xi )-\Phi (\alpha )}{Z}}}$
Mean	${\displaystyle \mu +{\frac {\varphi (\alpha )-\varphi (\beta )}{Z}}\sigma }$
Median	${\displaystyle \mu +\Phi ^{-1}\left({\frac {\Phi (\alpha )+\Phi (\beta )}{2}}\right)\sigma }$
Mode	${\displaystyle \left\{{\begin{array}{ll}a,&\mathrm {if} \ \mu <a\\\mu ,&\mathrm {if} \ a\leq \mu \leq b\\b,&\mathrm {if} \ \mu >b\end{array}}\right.}$
Variance	${\displaystyle \sigma ^{2}\left[1-{\frac {\beta \varphi (\beta )-\alpha \varphi (\alpha )}{Z}}-\left({\frac {\varphi (\alpha )-\varphi (\beta )}{Z}}\right)^{2}\right]}$
Entropy	${\displaystyle \ln({\sqrt {2\pi e}}\sigma Z)+{\frac {\alpha \varphi (\alpha )-\beta \varphi (\beta )}{2Z}}}$
MGF	${\displaystyle e^{\mu t+\sigma ^{2}t^{2}/2}\left[{\frac {\Phi (\beta -\sigma t)-\Phi (\alpha -\sigma t)}{\Phi (\beta )-\Phi (\alpha )}}\right]}$

In probability and statistics, the truncated normal distribution is the probability distribution derived from that of a normally distributed random variable by bounding the random variable from either below or above (or both). The truncated normal distribution has wide applications in statistics and econometrics.