Struct nuts_rs::DiagGradNutsSettings

pub struct DiagGradNutsSettings {
    pub num_tune: u64,
    pub num_draws: u64,
    pub maxdepth: u64,
    pub store_gradient: bool,
    pub store_unconstrained: bool,
    pub max_energy_error: f64,
    pub store_divergences: bool,
    pub mass_matrix_adapt: GradDiagOptions,
    pub check_turning: bool,
    pub num_chains: usize,
    pub seed: u64,
}

Settings for the NUTS sampler

Fields

num_tune: u64

The number of tuning steps, where we fit the step size and mass matrix.

num_draws: u64

The number of draws after tuning

maxdepth: u64

The maximum tree depth during sampling. The number of leapfrog steps is smaller than 2 ^ maxdepth.

store_gradient: bool

Store the gradient in the SampleStats

store_unconstrained: bool

Store each unconstrained parameter vector in the sampler stats

max_energy_error: f64

If the energy error is larger than this threshold we treat the leapfrog step as a divergence.

store_divergences: bool

Store detailed information about each divergence in the sampler stats

mass_matrix_adapt: GradDiagOptions

Settings for mass matrix adaptation.

check_turning: bool

num_chains: usize

seed: u64

Trait Implementations

impl Clone for DiagGradNutsSettings

fn clone(&self) -> DiagGradNutsSettings

Returns a copy of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Default for DiagGradNutsSettings

fn default() -> Self

Returns the “default value” for a type.

impl Settings for DiagGradNutsSettings

type Chain<M: Math> = NutsChain<M, EuclideanPotential<M, DiagMassMatrix<M>>, SmallRng, GradDiagStrategy<M>>

fn new_chain<M: Math, R: Rng + ?Sized>( &self, chain: u64, math: M, rng: &mut R ) -> Self::Chain<M>

fn sample_stats<M: Math>( &self, stats: &<Self::Chain<M> as SamplerStats<M>>::Stats ) -> SampleStats

fn hint_num_tune(&self) -> usize

fn hint_num_draws(&self) -> usize

fn num_chains(&self) -> usize

fn seed(&self) -> u64

impl Copy for DiagGradNutsSettings