SinhArcsinh - LostTech.TensorFlow Documentation

Type SinhArcsinh

Namespace tensorflow.contrib.distributions

The SinhArcsinh transformation of a distribution on `(-inf, inf)`.

This distribution models a random variable, making use of a `SinhArcsinh` transformation (which has adjustable tailweight and skew), a rescaling, and a shift.

The `SinhArcsinh` transformation of the Normal is described in great depth in [Sinh-arcsinh distributions](https://www.jstor.org/stable/27798865). Here we use a slightly different parameterization, in terms of `tailweight` and `skewness`. Additionally we allow for distributions other than Normal, and control over `scale` as well as a "shift" parameter `loc`.

#### Mathematical Details

Given random variable `Z`, we define the SinhArcsinh transformation of `Z`, `Y`, parameterized by `(loc, scale, skewness, tailweight)`, via the relation:

``` Y := loc + scale * F(Z) * (2 / F_0(2)) F(Z) := Sinh( (Arcsinh(Z) + skewness) * tailweight ) F_0(Z) := Sinh( Arcsinh(Z) * tailweight ) ```

This distribution is similar to the location-scale transformation `L(Z) := loc + scale * Z` in the following ways:

* If `skewness = 0` and `tailweight = 1` (the defaults), `F(Z) = Z`, and then `Y = L(Z)` exactly. * `loc` is used in both to shift the result by a constant factor. * The multiplication of `scale` by `2 / F_0(2)` ensures that if `skewness = 0` `P[Y - loc <= 2 * scale] = P[L(Z) - loc <= 2 * scale]`. Thus it can be said that the weights in the tails of `Y` and `L(Z)` beyond `loc + 2 * scale` are the same.

This distribution is different than `loc + scale * Z` due to the reshaping done by `F`:

* Positive (negative) `skewness` leads to positive (negative) skew. * positive skew means, the mode of `F(Z)` is "tilted" to the right. * positive skew means positive values of `F(Z)` become more likely, and negative values become less likely. * Larger (smaller) `tailweight` leads to fatter (thinner) tails. * Fatter tails mean larger values of `|F(Z)|` become more likely. * `tailweight < 1` leads to a distribution that is "flat" around `Y = loc`, and a very steep drop-off in the tails. * `tailweight > 1` leads to a distribution more peaked at the mode with heavier tails.

To see the argument about the tails, note that for `|Z| >> 1` and `|Z| >> (|skewness| * tailweight)**tailweight`, we have `Y approx 0.5 Z**tailweight e**(sign(Z) skewness * tailweight)`.

To see the argument regarding multiplying `scale` by `2 / F_0(2)`,

``` P[(Y - loc) / scale <= 2] = P[F(Z) * (2 / F_0(2)) <= 2] = P[F(Z) <= F_0(2)] = P[Z <= 2] (if F = F_0). ```

Properties

Public properties

object allow_nan_stats get;

object allow_nan_stats_dyn get;

TensorShape batch_shape get;

object batch_shape_dyn get;

object bijector get;

object bijector_dyn get;

object distribution get;

object distribution_dyn get;

object dtype get;

object dtype_dyn get;

TensorShape event_shape get;

object event_shape_dyn get;

object loc get;

The `loc` in `Y := loc + scale @ F(Z) * (2 / F(2)).

object loc_dyn get;

The `loc` in `Y := loc + scale @ F(Z) * (2 / F(2)).

string name get;

object name_dyn get;

IDictionary<object, object> parameters get;

object parameters_dyn get;

object PythonObject get;

object reparameterization_type get;

object reparameterization_type_dyn get;

object scale get;

The `LinearOperator` `scale` in `Y := loc + scale @ F(Z) * (2 / F(2)).

object scale_dyn get;