josiann.moves.sequential.Metropolis
- class josiann.moves.sequential.Metropolis(*, variances, bounds=None, repr_attributes=(), **kwargs)[source]
Metropolis step obtained from a multivariate normal distribution with mean <x> and covariance matrix <variances>
Instantiate a Move.
- Parameters:
variances (
ndarray
[Any
,dtype
[float64
]]) – list of variances between dimensions, which will be set as the diagonal of the covariance matrix.bounds (
Optional
[ndarray
[Any
,dtype
[Union
[float64
,int64
]]]] (default:None
)) – optional sequence of (min, max) bounds for values to propose in each dimension.repr_attributes (
tuple
[str
,...
] (default:()
)) – list of attribute names to include in the string representation of this Move.kwargs (Any) –
Methods
Instantiate a Move.
Generate a new proposed vector x.
Set bounds for the move.
Methods
- Metropolis.__init__(*, variances, bounds=None, repr_attributes=(), **kwargs)[source]
Instantiate a Move.
- Parameters:
variances (
ndarray
[Any
,dtype
[float64
]]) – list of variances between dimensions, which will be set as the diagonal of the covariance matrix.bounds (
Optional
[ndarray
[Any
,dtype
[Union
[float64
,int64
]]]] (default:None
)) – optional sequence of (min, max) bounds for values to propose in each dimension.repr_attributes (
tuple
[str
,...
] (default:()
)) – list of attribute names to include in the string representation of this Move.kwargs (Any) –
- Metropolis.get_proposal(x, state)
Generate a new proposed vector x.