# skfolio.prior.ReturnDistribution ### *class* skfolio.prior.ReturnDistribution(mu, covariance, returns, sample_weight=None, factor_model=None) Return distribution estimated by a prior estimator. Prior estimators always return the **full universe** (all assets that have ever been part of the investment universe). Assets that are not investable at the current point in time (e.g. delisted, not yet listed, warm-up period) are represented with `NaN` in `mu`, `covariance`, and/or `returns`. An asset is considered investable when both `mu[i]` and `covariance[i, i]` are finite. The `investable_mask` property infers this condition on first access and reconciles warm-up periods across independent moment estimators. NaN values in `returns` are restricted to the columns of non-investable assets. Prior estimators resolve missing observations of investable assets into finite scenario values (for example, [`EmpiricalPrior`](https://skfolio.org/generated/skfolio.prior.EmpiricalPrior.html.md#skfolio.prior.EmpiricalPrior) zero-fills them), so that `investable_subset` returns fully finite arrays. Use `investable_subset` before passing the distribution to downstream routines that operate only on the investable universe. * **Attributes:** **mu** *ndarray of shape (n_assets,)* : Estimation of expected asset returns. **covariance** *ndarray of shape (n_assets, n_assets)* : Estimation of the assets covariance matrix. **returns** *ndarray of shape (n_observations, n_assets)* : Estimation of the assets returns. **sample_weight** *ndarray of shape (n_observations,), optional* : Sample weights for each observation. If `None`, equal weights are assumed. **factor_model** *FactorModel, optional* : Factor model decomposition and diagnostics. The default is `None`. ### Methods | [`investable_subset`](#skfolio.prior.ReturnDistribution.investable_subset)([slim]) | Return a `ReturnDistribution` restricted to investable assets. | |------------------------------------------------------------------------------|------------------------------------------------------------------| #### *property* covariance_sqrt Covariance square root for SOC-based optimization. When a [`FactorModel`](https://skfolio.org/generated/skfolio.prior.FactorModel.html.md#skfolio.prior.FactorModel) is available, delegates to [`FactorModel.covariance_sqrt`](https://skfolio.org/generated/skfolio.prior.FactorModel.html.md#skfolio.prior.FactorModel.covariance_sqrt) to exploit the low-rank factor structure. Otherwise, falls back to the Cholesky decomposition of `covariance`. When non-investable assets are represented with `NaN` entries and no factor model is available, callers should apply `investable_subset` first. * **Returns:** CovarianceSqrt #### *property* investable_mask Boolean mask where `True` marks investable assets. The mask is inferred as the intersection of finite `mu` values and a finite diagonal in `covariance`. Returns `None` when all assets are investable. * **Raises:** ValueError : If no asset is investable (all `NaN` in `mu` and/or `covariance`). #### investable_subset(slim=False) Return a `ReturnDistribution` restricted to investable assets. * **Parameters:** **slim** *bool, default=False* : When `True`, heavy diagnostic fields on the nested `FactorModel` (e.g. `exposures`, `idio_returns`, `idio_variances`, `benchmark_weights`) are set to `None` to reduce memory usage. This is typically used by optimization estimators that only need `loading_matrix`, covariance, and return series. * **Returns:** **subset** *ReturnDistribution* : Distribution over the investable assets only. If all assets are already investable and `slim=False`, `self` is returned. #### *property* n_assets Total number of assets in the full universe. #### *property* n_investable_assets Number of investable assets.