Math

This submodule contains various mathematical functions. Most of them are imported directly from aesara.tensor (see there for more details). Doing any kind of math with PyMC random variables, or defining custom likelihoods or priors requires you to use these Aesara expressions rather than NumPy or Python code.

Functions exposed in pymc namespace

expand_packed_triangular(n, packed[, lower, ...])	Convert a packed triangular matrix into a two dimensional array.
logit(p)
invlogit(x[, eps])	The inverse of the logit function, 1 / (1 + exp(-x)).
probit(p)
invprobit(x)
logsumexp(x[, axis, keepdims])	Compute the log of the sum of exponentials of input elements.

Functions exposed in pymc.math

dot(l, r)	Return a symbolic dot product.
constant(x[, name, ndim, dtype])	Return a TensorConstant with value x.
flatten(x[, ndim])	Return a copy of the array collapsed into one dimension.
zeros_like(model[, dtype, opt])	equivalent of numpy.zeros_like Parameters ---------- model : tensor dtype : data-type, optional opt : If True, we will return a constant instead of a graph when possible. Useful for Aesara optimization, not for user building a graph as this have the consequence that model isn't always in the graph.
ones_like(model[, dtype, opt])	equivalent of numpy.ones_like Parameters ---------- model : tensor dtype : data-type, optional opt : If True, we will return a constant instead of a graph when possible. Useful for Aesara optimization, not for user building a graph as this have the consequence that model isn't always in the graph.
stack(*tensors, **kwargs)	Stack tensors in sequence on given axis (default is 0).
concatenate(tensor_list[, axis])	Alias for `join`(axis, *tensor_list).
sum(input[, axis, dtype, keepdims, acc_dtype])	Computes the sum along the given axis(es) of a tensor input.
prod(input[, axis, dtype, keepdims, ...])	Computes the product along the given axis(es) of a tensor input.
lt	a < b Generalizes a scalar Op to tensors.
gt	a > b Generalizes a scalar Op to tensors.
le	a <= b Generalizes a scalar Op to tensors.
ge	a >= b Generalizes a scalar Op to tensors.
eq	a == b Generalizes a scalar Op to tensors.
neq	a != b Generalizes a scalar Op to tensors.
switch	if cond then ift else iff Generalizes a scalar Op to tensors.
clip	Clip x to be between min and max.
where	if cond then ift else iff Generalizes a scalar Op to tensors.
and_	bitwise a & b Generalizes a scalar Op to tensors.
or_	bitwise a | b Generalizes a scalar Op to tensors.
exp	e^`a` Generalizes a scalar Op to tensors.
log	base e logarithm of a Generalizes a scalar Op to tensors.
cos	cosine of a Generalizes a scalar Op to tensors.
sin	sine of a Generalizes a scalar Op to tensors.
tan	tangent of a Generalizes a scalar Op to tensors.
cosh	hyperbolic cosine of a Generalizes a scalar Op to tensors.
sinh	hyperbolic sine of a Generalizes a scalar Op to tensors.
tanh	hyperbolic tangent of a Generalizes a scalar Op to tensors.
sqr	square of a Generalizes a scalar Op to tensors.
sqrt	square root of a Generalizes a scalar Op to tensors.
erf	error function Generalizes a scalar Op to tensors.
erfinv	inverse error function Generalizes a scalar Op to tensors.
dot(l, r)	Return a symbolic dot product.
maximum	elemwise maximum.
minimum	elemwise minimum.
sgn	sign of a Generalizes a scalar Op to tensors.
ceil	ceiling of a Generalizes a scalar Op to tensors.
floor	floor of a Generalizes a scalar Op to tensors.
det	Matrix determinant.
matrix_inverse	Computes the inverse of a matrix \(A\).
extract_diag	Return specified diagonals.
matrix_dot(*args)	Shorthand for product between several dots.
trace(X)	Returns the sum of diagonal elements of matrix X.
sigmoid	Logistic sigmoid function (1 / (1 + exp(x)), also known as expit or inverse logit Generalizes a scalar Op to tensors.
logsumexp(x[, axis, keepdims])	Compute the log of the sum of exponentials of input elements.
invlogit(x[, eps])	The inverse of the logit function, 1 / (1 + exp(-x)).
logit(p)