Continuous¶

 Uniform([lower, upper, transform]) Continuous uniform log-likelihood. Flat(*args, **kwargs) Uninformative log-likelihood that returns 0 regardless of the passed value. HalfFlat(*args, **kwargs) Improper flat prior over the positive reals. Normal([mu, sd, tau]) Univariate normal log-likelihood. HalfNormal([sd, tau]) Half-normal log-likelihood. SkewNormal([mu, sd, tau, alpha]) Univariate skew-normal log-likelihood. Beta([alpha, beta, mu, sd]) Beta log-likelihood. Exponential(lam, *args, **kwargs) Exponential log-likelihood. Laplace(mu, b, *args, **kwargs) Laplace log-likelihood. StudentT(nu[, mu, lam, sd]) Student’s T log-likelihood. HalfStudentT([nu, sd, lam]) Half Student’s T log-likelihood Cauchy(alpha, beta, *args, **kwargs) Cauchy log-likelihood. HalfCauchy(beta, *args, **kwargs) Half-Cauchy log-likelihood. Gamma([alpha, beta, mu, sd]) Gamma log-likelihood. Weibull(alpha, beta, *args, **kwargs) Weibull log-likelihood. Lognormal([mu, sd, tau]) Log-normal log-likelihood. ChiSquared(nu, *args, **kwargs) $$\chi^2$$ log-likelihood. Wald([mu, lam, phi, alpha]) Wald log-likelihood. Pareto(alpha, m, *args, **kwargs) Pareto log-likelihood. InverseGamma(alpha[, beta]) Inverse gamma log-likelihood, the reciprocal of the gamma distribution. ExGaussian(mu, sigma, nu, *args, **kwargs) Exponentially modified Gaussian log-likelihood. VonMises([mu, kappa, transform]) Univariate VonMises log-likelihood. Triangular([lower, upper, c]) Continuous Triangular log-likelihood Implemented by J. Gumbel([mu, beta]) Univariate Gumbel log-likelihood Interpolated(x_points, pdf_points[, transform]) Univariate probability distribution defined as a linear interpolation of probability density function evaluated on some lattice of points.

pymc3.distributions

A collection of common probability distributions for stochastic nodes in PyMC.

class pymc3.distributions.continuous.Uniform(lower=0, upper=1, transform='interval', *args, **kwargs)

Continuous uniform log-likelihood.

$f(x \mid lower, upper) = \frac{1}{upper-lower}$ Support $$x \in [lower, upper]$$ Mean $$\dfrac{lower + upper}{2}$$ Variance $$\dfrac{(upper - lower)^2}{12}$$
Parameters: lower (float) – Lower limit. upper (float) – Upper limit.
class pymc3.distributions.continuous.Flat(*args, **kwargs)

Uninformative log-likelihood that returns 0 regardless of the passed value.

class pymc3.distributions.continuous.HalfFlat(*args, **kwargs)

Improper flat prior over the positive reals.

class pymc3.distributions.continuous.Normal(mu=0, sd=None, tau=None, **kwargs)

Univariate normal log-likelihood.

$f(x \mid \mu, \tau) = \sqrt{\frac{\tau}{2\pi}} \exp\left\{ -\frac{\tau}{2} (x-\mu)^2 \right\}$
 Support $$x \in \mathbb{R}$$ Mean $$\mu$$ Variance $$\dfrac{1}{\tau}$$ or $$\sigma^2$$

Normal distribution can be parameterized either in terms of precision or standard deviation. The link between the two parametrizations is given by

$\tau = \dfrac{1}{\sigma^2}$ Parameters: mu (float) – Mean. sd (float) – Standard deviation (sd > 0). tau (float) – Precision (tau > 0).
class pymc3.distributions.continuous.Beta(alpha=None, beta=None, mu=None, sd=None, *args, **kwargs)

Beta log-likelihood.

$f(x \mid \alpha, \beta) = \frac{x^{\alpha - 1} (1 - x)^{\beta - 1}}{B(\alpha, \beta)}$ Support $$x \in (0, 1)$$ Mean $$\dfrac{\alpha}{\alpha + \beta}$$ Variance $$\dfrac{\alpha \beta}{(\alpha+\beta)^2(\alpha+\beta+1)}$$

Beta distribution can be parameterized either in terms of alpha and beta or mean and standard deviation. The link between the two parametrizations is given by

\begin{align}\begin{aligned}\begin{split}\alpha &= \mu \kappa \\ \beta &= (1 - \mu) \kappa\end{split}\\\text{where } \kappa = \frac{\mu(1-\mu)}{\sigma^2} - 1\end{aligned}\end{align}
Parameters: alpha (float) – alpha > 0. beta (float) – beta > 0. mu (float) – Alternative mean (0 < mu < 1). sd (float) – Alternative standard deviation (0 < sd < sqrt(mu * (1 - mu))).

Notes

Beta distribution is a conjugate prior for the parameter $$p$$ of the binomial distribution.

class pymc3.distributions.continuous.Exponential(lam, *args, **kwargs)

Exponential log-likelihood.

$f(x \mid \lambda) = \lambda \exp\left\{ -\lambda x \right\}$ Support $$x \in [0, \infty)$$ Mean $$\dfrac{1}{\lambda}$$ Variance $$\dfrac{1}{\lambda^2}$$
Parameters: lam (float) – Rate or inverse scale (lam > 0)
class pymc3.distributions.continuous.Laplace(mu, b, *args, **kwargs)

Laplace log-likelihood.

$f(x \mid \mu, b) = \frac{1}{2b} \exp \left\{ - \frac{|x - \mu|}{b} \right\}$ Support $$x \in \mathbb{R}$$ Mean $$\mu$$ Variance $$2 b^2$$
Parameters: mu (float) – Location parameter. b (float) – Scale parameter (b > 0).
class pymc3.distributions.continuous.StudentT(nu, mu=0, lam=None, sd=None, *args, **kwargs)

Student’s T log-likelihood.

Describes a normal variable whose precision is gamma distributed. If only nu parameter is passed, this specifies a standard (central) Student’s T.

$f(x|\mu,\lambda,\nu) = \frac{\Gamma(\frac{\nu + 1}{2})}{\Gamma(\frac{\nu}{2})} \left(\frac{\lambda}{\pi\nu}\right)^{\frac{1}{2}} \left[1+\frac{\lambda(x-\mu)^2}{\nu}\right]^{-\frac{\nu+1}{2}}$ Support $$x \in \mathbb{R}$$
Parameters: nu (float) – Degrees of freedom, also known as normality parameter (nu > 0). mu (float) – Location parameter. lam (float) – Scale parameter (lam > 0).
class pymc3.distributions.continuous.Cauchy(alpha, beta, *args, **kwargs)

Cauchy log-likelihood.

Also known as the Lorentz or the Breit-Wigner distribution.

$f(x \mid \alpha, \beta) = \frac{1}{\pi \beta [1 + (\frac{x-\alpha}{\beta})^2]}$ Support $$x \in \mathbb{R}$$ Mode $$\alpha$$ Mean undefined Variance undefined
Parameters: alpha (float) – Location parameter beta (float) – Scale parameter > 0
class pymc3.distributions.continuous.HalfCauchy(beta, *args, **kwargs)

Half-Cauchy log-likelihood.

$f(x \mid \beta) = \frac{2}{\pi \beta [1 + (\frac{x}{\beta})^2]}$ Support $$x \in \mathbb{R}$$ Mode 0 Mean undefined Variance undefined
Parameters: beta (float) – Scale parameter (beta > 0).
class pymc3.distributions.continuous.Gamma(alpha=None, beta=None, mu=None, sd=None, *args, **kwargs)

Gamma log-likelihood.

Represents the sum of alpha exponentially distributed random variables, each of which has mean beta.

$f(x \mid \alpha, \beta) = \frac{\beta^{\alpha}x^{\alpha-1}e^{-\beta x}}{\Gamma(\alpha)}$ Support $$x \in (0, \infty)$$ Mean $$\dfrac{\alpha}{\beta}$$ Variance $$\dfrac{\alpha}{\beta^2}$$

Gamma distribution can be parameterized either in terms of alpha and beta or mean and standard deviation. The link between the two parametrizations is given by

$\begin{split}\alpha &= \frac{\mu^2}{\sigma^2} \\ \beta &= \frac{\mu}{\sigma^2}\end{split}$
Parameters: alpha (float) – Shape parameter (alpha > 0). beta (float) – Rate parameter (beta > 0). mu (float) – Alternative shape parameter (mu > 0). sd (float) – Alternative scale parameter (sd > 0).
class pymc3.distributions.continuous.Weibull(alpha, beta, *args, **kwargs)

Weibull log-likelihood.

$f(x \mid \alpha, \beta) = \frac{\alpha x^{\alpha - 1} \exp(-(\frac{x}{\beta})^{\alpha})}{\beta^\alpha}$ Support $$x \in [0, \infty)$$ Mean $$\beta \Gamma(1 + \frac{1}{\alpha})$$ Variance $$\beta^2 \Gamma(1 + \frac{2}{\alpha} - \mu^2)$$
Parameters: alpha (float) – Shape parameter (alpha > 0). beta (float) – Scale parameter (beta > 0).
class pymc3.distributions.continuous.HalfStudentT(nu=1, sd=None, lam=None, *args, **kwargs)

Half Student’s T log-likelihood

$f(x \mid \sigma,\nu) = \frac{2\;\Gamma\left(\frac{\nu+1}{2}\right)} {\Gamma\left(\frac{\nu}{2}\right)\sqrt{\nu\pi\sigma^2}} \left(1+\frac{1}{\nu}\frac{x^2}{\sigma^2}\right)^{-\frac{\nu+1}{2}}$ Support $$x \in [0, \infty)$$
Parameters: nu (float) – Degrees of freedom, also known as normality parameter (nu > 0). sd (float) – Scale parameter (sd > 0). Converges to the standard deviation as nu increases lam (float) – Scale parameter (lam > 0). Converges to the precision as nu increases
class pymc3.distributions.continuous.Lognormal(mu=0, sd=None, tau=None, *args, **kwargs)

Log-normal log-likelihood.

Distribution of any random variable whose logarithm is normally distributed. A variable might be modeled as log-normal if it can be thought of as the multiplicative product of many small independent factors.

$f(x \mid \mu, \tau) = \frac{1}{x} \sqrt{\frac{\tau}{2\pi}} \exp\left\{ -\frac{\tau}{2} (\ln(x)-\mu)^2 \right\}$ Support $$x \in [0, \infty)$$ Mean $$\exp\{\mu + \frac{1}{2\tau}\}$$ Variance $$(\exp\{\frac{1}{\tau}\} - 1) \times \exp\{2\mu + \frac{1}{\tau}\}$$
Parameters: mu (float) – Location parameter. tau (float) – Scale parameter (tau > 0).
class pymc3.distributions.continuous.ChiSquared(nu, *args, **kwargs)

$$\chi^2$$ log-likelihood.

$f(x \mid \nu) = \frac{x^{(\nu-2)/2}e^{-x/2}}{2^{\nu/2}\Gamma(\nu/2)}$ Support $$x \in [0, \infty)$$ Mean $$\nu$$ Variance $$2 \nu$$
Parameters: nu (int) – Degrees of freedom (nu > 0).
class pymc3.distributions.continuous.HalfNormal(sd=None, tau=None, *args, **kwargs)

Half-normal log-likelihood.

$f(x \mid \tau) = \sqrt{\frac{2\tau}{\pi}} \exp\left\{ {\frac{-x^2 \tau}{2}}\right\}$ Support $$x \in [0, \infty)$$ Mean $$0$$ Variance $$\dfrac{1}{\tau}$$ or $$\sigma^2$$
Parameters: sd (float) – Standard deviation (sd > 0). tau (float) – Precision (tau > 0).
class pymc3.distributions.continuous.Wald(mu=None, lam=None, phi=None, alpha=0.0, *args, **kwargs)

Wald log-likelihood.

$f(x \mid \mu, \lambda) = \left(\frac{\lambda}{2\pi)}\right)^{1/2} x^{-3/2} \exp\left\{ -\frac{\lambda}{2x}\left(\frac{x-\mu}{\mu}\right)^2 \right\}$ Support $$x \in (0, \infty)$$ Mean $$\mu$$ Variance $$\dfrac{\mu^3}{\lambda}$$

Wald distribution can be parameterized either in terms of lam or phi. The link between the two parametrizations is given by

$\phi = \dfrac{\lambda}{\mu}$
Parameters: mu (float, optional) – Mean of the distribution (mu > 0). lam (float, optional) – Relative precision (lam > 0). phi (float, optional) – Alternative shape parameter (phi > 0). alpha (float, optional) – Shift/location parameter (alpha >= 0).

Notes

To instantiate the distribution specify any of the following

• only mu (in this case lam will be 1)
• mu and lam
• mu and phi
• lam and phi

References

 [Tweedie1957] Tweedie, M. C. K. (1957). Statistical Properties of Inverse Gaussian Distributions I. The Annals of Mathematical Statistics, Vol. 28, No. 2, pp. 362-377
 [Michael1976] Michael, J. R., Schucany, W. R. and Hass, R. W. (1976). Generating Random Variates Using Transformations with Multiple Roots. The American Statistician, Vol. 30, No. 2, pp. 88-90
class pymc3.distributions.continuous.Pareto(alpha, m, *args, **kwargs)

Pareto log-likelihood.

Often used to characterize wealth distribution, or other examples of the 80/20 rule.

$f(x \mid \alpha, m) = \frac{\alpha m^{\alpha}}{x^{\alpha+1}}$ Support $$x \in [m, \infty)$$ Mean $$\dfrac{\alpha m}{\alpha - 1}$$ for $$\alpha \ge 1$$ Variance $$\dfrac{m \alpha}{(\alpha - 1)^2 (\alpha - 2)}$$ for $$\alpha > 2$$
Parameters: alpha (float) – Shape parameter (alpha > 0). m (float) – Scale parameter (m > 0).
class pymc3.distributions.continuous.InverseGamma(alpha, beta=1, *args, **kwargs)

Inverse gamma log-likelihood, the reciprocal of the gamma distribution.

$f(x \mid \alpha, \beta) = \frac{\beta^{\alpha}}{\Gamma(\alpha)} x^{-\alpha - 1} \exp\left(\frac{-\beta}{x}\right)$ Support $$x \in (0, \infty)$$ Mean $$\dfrac{\beta}{\alpha-1}$$ for $$\alpha > 1$$ Variance $$\dfrac{\beta^2}{(\alpha-1)^2(\alpha)}$$ for $$\alpha > 2$$
Parameters: alpha (float) – Shape parameter (alpha > 0). beta (float) – Scale parameter (beta > 0).
class pymc3.distributions.continuous.ExGaussian(mu, sigma, nu, *args, **kwargs)

Exponentially modified Gaussian log-likelihood.

Results from the convolution of a normal distribution with an exponential distribution.

$f(x \mid \mu, \sigma, \tau) = \frac{1}{\nu}\; \exp\left\{\frac{\mu-x}{\nu}+\frac{\sigma^2}{2\nu^2}\right\} \Phi\left(\frac{x-\mu}{\sigma}-\frac{\sigma}{\nu}\right)$

where $$\Phi$$ is the cumulative distribution function of the standard normal distribution. Support $$x \in \mathbb{R}$$ Mean $$\mu + \nu$$ Variance $$\sigma^2 + \nu^2$$
Parameters: mu (float) – Mean of the normal distribution. sigma (float) – Standard deviation of the normal distribution (sigma > 0). nu (float) – Mean of the exponential distribution (nu > 0).

References

 [Rigby2005] Rigby R.A. and Stasinopoulos D.M. (2005). “Generalized additive models for location, scale and shape” Applied Statististics., 54, part 3, pp 507-554.
 [Lacouture2008] Lacouture, Y. and Couseanou, D. (2008). “How to use MATLAB to fit the ex-Gaussian and other probability functions to a distribution of response times”. Tutorials in Quantitative Methods for Psychology, Vol. 4, No. 1, pp 35-45.
class pymc3.distributions.continuous.VonMises(mu=0.0, kappa=None, transform='circular', *args, **kwargs)

Univariate VonMises log-likelihood.

$f(x \mid \mu, \kappa) = \frac{e^{\kappa\cos(x-\mu)}}{2\pi I_0(\kappa)}$

where $$I_0$$ is the modified Bessel function of order 0. Support $$x \in [-\pi, \pi]$$ Mean $$\mu$$ Variance $$1-\frac{I_1(\kappa)}{I_0(\kappa)}$$
Parameters: mu (float) – Mean. kappa (float) – Concentration (frac{1}{kappa} is analogous to sigma^2).
class pymc3.distributions.continuous.SkewNormal(mu=0.0, sd=None, tau=None, alpha=1, *args, **kwargs)

Univariate skew-normal log-likelihood.

$f(x \mid \mu, \tau, \alpha) = 2 \Phi((x-\mu)\sqrt{\tau}\alpha) \phi(x,\mu,\tau)$ Support $$x \in \mathbb{R}$$ Mean $$\mu + \sigma \sqrt{\frac{2}{\pi}} \frac {\alpha }{{\sqrt {1+\alpha ^{2}}}}$$ Variance $$\sigma^2 \left( 1-\frac{2\alpha^2}{(\alpha^2+1) \pi} \right)$$

Skew-normal distribution can be parameterized either in terms of precision or standard deviation. The link between the two parametrizations is given by

$\tau = \dfrac{1}{\sigma^2}$
Parameters: mu (float) – Location parameter. sd (float) – Scale parameter (sd > 0). tau (float) – Alternative scale parameter (tau > 0). alpha (float) – Skewness parameter.

Notes

When alpha=0 we recover the Normal distribution and mu becomes the mean, tau the precision and sd the standard deviation. In the limit of alpha approaching plus/minus infinite we get a half-normal distribution.

class pymc3.distributions.continuous.Logistic(mu=0.0, s=1.0, *args, **kwargs)

Logistic log-likelihood.

$f(x \mid \mu, s) = \frac{\exp\left(-\frac{x - \mu}{s}\right)}{s \left(1 + \exp\left(-\frac{x - \mu}{s}\right)\right)^2}$
 Support $$x \in \mathbb{R}$$ Mean $$\mu$$ Variance $$\frac{s^2 \pi^2}{3}$$ Parameters: mu (float) – Mean. s (float) – Scale (s > 0).
class pymc3.distributions.continuous.Interpolated(x_points, pdf_points, transform='interval', *args, **kwargs)

Univariate probability distribution defined as a linear interpolation of probability density function evaluated on some lattice of points.

The lattice can be uneven, so the steps between different points can have different size and it is possible to vary the precision between regions of the support.

The probability density function values don not have to be normalized, as the interpolated density is any way normalized to make the total probability equal to $1$.

Both parameters x_points and values pdf_points are not variables, but plain array-like objects, so they are constant and cannot be sampled.

 Support $$x \in [x\_points, x\_points[-1]]$$
Parameters: x_points (array-like) – A monotonically growing list of values pdf_points (array-like) – Probability density function evaluated on lattice x_points