# jax.scipy package¶

## jax.scipy.fft¶

 `dct`(x[, type, n, axis, norm]) Return the Discrete Cosine Transform of arbitrary type sequence x. `dctn`(x[, type, s, axes, norm]) Return multidimensional Discrete Cosine Transform along the specified axes.

## jax.scipy.linalg¶

 `block_diag`(*arrs) Create a block diagonal matrix from provided arrays. `cho_factor`(a[, lower, overwrite_a, check_finite]) Compute the Cholesky decomposition of a matrix, to use in cho_solve `cho_solve`(c_and_lower, b[, overwrite_b, …]) Solve the linear equations A x = b, given the Cholesky factorization of A. `cholesky`(a[, lower, overwrite_a, check_finite]) Compute the Cholesky decomposition of a matrix. `det`(a[, overwrite_a, check_finite]) Compute the determinant of a matrix `eigh`(a[, b, lower, eigvals_only, …]) Solve a standard or generalized eigenvalue problem for a complex `expm`(A, *[, upper_triangular, max_squarings]) Compute the matrix exponential using Pade approximation. `expm_frechet`(A, E, *[, method, compute_expm]) Frechet derivative of the matrix exponential of A in the direction E. `inv`(a[, overwrite_a, check_finite]) Compute the inverse of a matrix. `lu`(a[, permute_l, overwrite_a, check_finite]) Compute pivoted LU decomposition of a matrix. `lu_factor`(a[, overwrite_a, check_finite]) Compute pivoted LU decomposition of a matrix. `lu_solve`(lu_and_piv, b[, trans, …]) Solve an equation system, a x = b, given the LU factorization of a `qr`(a[, overwrite_a, lwork, mode, pivoting, …]) Compute QR decomposition of a matrix. `solve`(a, b[, sym_pos, lower, overwrite_a, …]) Solves the linear equation set `a * x = b` for the unknown `x` `solve_triangular`(a, b[, trans, lower, …]) Solve the equation a x = b for x, assuming a is a triangular matrix. `svd`(a[, full_matrices, compute_uv, …]) Singular Value Decomposition. `tril`(m[, k]) Make a copy of a matrix with elements above the kth diagonal zeroed. `triu`(m[, k]) Make a copy of a matrix with elements below the kth diagonal zeroed.

## jax.scipy.ndimage¶

 `map_coordinates`(input, coordinates, order[, …]) Map the input array to new coordinates by interpolation.

## jax.scipy.optimize¶

 `minimize`(fun, x0[, args, tol, options]) Minimization of scalar function of one or more variables. `OptimizeResults`(x, success, …) Object holding optimization results.

## jax.scipy.signal¶

 `convolve`(in1, in2[, mode, method, precision]) Convolve two N-dimensional arrays. `convolve2d`(in1, in2[, mode, boundary, …]) Convolve two 2-dimensional arrays. `correlate`(in1, in2[, mode, method, precision]) Cross-correlate two N-dimensional arrays. `correlate2d`(in1, in2[, mode, boundary, …]) Cross-correlate two 2-dimensional arrays.

## jax.scipy.sparse.linalg¶

 `bicgstab`(A, b[, x0, tol, atol, maxiter, M]) Use Bi-Conjugate Gradient Stable iteration to solve `Ax = b`. `cg`(A, b[, x0, tol, atol, maxiter, M]) Use Conjugate Gradient iteration to solve `Ax = b`. `gmres`(A, b[, x0, tol, atol, restart, …]) GMRES solves the linear system A x = b for x, given A and b.

## jax.scipy.special¶

 `betainc`(a, b, x) Incomplete beta function. The digamma function. Elementwise function for computing entropy. Returns the error function of complex argument. Complementary error function, `1 - erf(x)`. Inverse of the error function. Exponential integral E1. `expi` Exponential integral Ei. `expit` Expit (a.k.a. `expn` Generalized exponential integral En. `gammainc`(a, x) Regularized lower incomplete gamma function. `gammaincc`(a, x) Regularized upper incomplete gamma function. Logarithm of the absolute value of the gamma function. `i0`(x) Modified Bessel function of order 0. Exponentially scaled modified Bessel function of order 0. `i1`(x) Modified Bessel function of order 1. Exponentially scaled modified Bessel function of order 1. `log_ndtr` Log Normal distribution function. `logit` Logit ufunc for ndarrays. `logsumexp`(a[, axis, b, keepdims, return_sign]) Compute the log of the sum of exponentials of input elements. `lpmn`(m, n, z) The associated Legendre functions (ALFs) of the first kind. `lpmn_values`(m, n, z, is_normalized) The associated Legendre functions (ALFs) of the first kind. `multigammaln`(a, d) Returns the log of multivariate gamma, also sometimes called the Normal distribution function. The inverse of the CDF of the Normal distribution function. `polygamma`(n, x) Polygamma functions. `sph_harm`(m, n, theta, phi[, n_max]) Computes the spherical harmonics. `xlog1py`(x, y) Compute `x*log1p(y)` so that the result is 0 if `x = 0`. `xlogy`(x, y) Compute `x*log(y)` so that the result is 0 if `x = 0`. `zeta`(x[, q]) Riemann or Hurwitz zeta function.

## jax.scipy.stats¶

### jax.scipy.stats.bernoulli¶

 `logpmf`(k, p[, loc]) Log of the probability mass function at k of the given RV. `pmf`(k, p[, loc]) Probability mass function at k of the given RV.

### jax.scipy.stats.beta¶

 `logpdf`(x, a, b[, loc, scale]) Log of the probability density function at x of the given RV. `pdf`(x, a, b[, loc, scale]) Probability density function at x of the given RV.

### jax.scipy.stats.betabinom¶

 `logpmf`(k, n, a, b[, loc]) Log of the probability mass function at k of the given RV. `pmf`(k, n, a, b[, loc]) Probability mass function at k of the given RV.

### jax.scipy.stats.cauchy¶

 `logpdf`(x[, loc, scale]) Log of the probability density function at x of the given RV. `pdf`(x[, loc, scale]) Probability density function at x of the given RV.

### jax.scipy.stats.chi2¶

 `logpdf`(x, df[, loc, scale]) Log of the probability density function at x of the given RV. `pdf`(x, df[, loc, scale]) Probability density function at x of the given RV.

### jax.scipy.stats.dirichlet¶

 `logpdf`(x, alpha) Log of the Dirichlet probability density function. `pdf`(x, alpha) The Dirichlet probability density function.

### jax.scipy.stats.expon¶

 `logpdf`(x[, loc, scale]) Log of the probability density function at x of the given RV. `pdf`(x[, loc, scale]) Probability density function at x of the given RV.

### jax.scipy.stats.gamma¶

 `logpdf`(x, a[, loc, scale]) Log of the probability density function at x of the given RV. `pdf`(x, a[, loc, scale]) Probability density function at x of the given RV.

### jax.scipy.stats.geom¶

 `logpmf`(k, p[, loc]) Log of the probability mass function at k of the given RV. `pmf`(k, p[, loc]) Probability mass function at k of the given RV.

### jax.scipy.stats.laplace¶

 `cdf`(x[, loc, scale]) Cumulative distribution function of the given RV. `logpdf`(x[, loc, scale]) Log of the probability density function at x of the given RV. `pdf`(x[, loc, scale]) Probability density function at x of the given RV.

### jax.scipy.stats.logistic¶

 Cumulative distribution function of the given RV. Inverse survival function (inverse of sf) at q of the given RV. Log of the probability density function at x of the given RV. Probability density function at x of the given RV. Percent point function (inverse of cdf) at q of the given RV. `sf`(x) Survival function (1 - cdf) at x of the given RV.

### jax.scipy.stats.multivariate_normal¶

 `logpdf`(x, mean, cov[, allow_singular]) Log of the multivariate normal probability density function. `pdf`(x, mean, cov) Multivariate normal probability density function.

### jax.scipy.stats.norm¶

 `cdf`(x[, loc, scale]) Cumulative distribution function of the given RV. `logcdf`(x[, loc, scale]) Log of the cumulative distribution function at x of the given RV. `logpdf`(x[, loc, scale]) Log of the probability density function at x of the given RV. `pdf`(x[, loc, scale]) Probability density function at x of the given RV. `ppf`(q[, loc, scale]) Percent point function (inverse of cdf) at q of the given RV.

### jax.scipy.stats.pareto¶

 `logpdf`(x, b[, loc, scale]) Log of the probability density function at x of the given RV. `pdf`(x, b[, loc, scale]) Probability density function at x of the given RV.

### jax.scipy.stats.poisson¶

 `logpmf`(k, mu[, loc]) Log of the probability mass function at k of the given RV. `pmf`(k, mu[, loc]) Probability mass function at k of the given RV.

### jax.scipy.stats.t¶

 `logpdf`(x, df[, loc, scale]) Log of the probability density function at x of the given RV. `pdf`(x, df[, loc, scale]) Probability density function at x of the given RV.

### jax.scipy.stats.uniform¶

 `logpdf`(x[, loc, scale]) Log of the probability density function at x of the given RV. `pdf`(x[, loc, scale]) Probability density function at x of the given RV.