Create basic model

Create a basic monty model. This takes a user-supplied object that minimally can compute a probability density (via a density function) and information about parameters; with this we can sample from the model using MCMC using monty_sample. We don't imagine that many users will call this function directly, but that this will be glue used by packages.

Usage

monty_model(model, properties = NULL)

Arguments

model

A list or environment with elements as described in Details.

properties

Optionally, a monty_model_properties object, used to enforce or clarify properties of the model.

Value

An object of class monty_model. This will have elements:

• model: The model as provided

• parameters: The parameter name vector

• parameter_groups: The parameter groups

• domain: The parameter domain matrix, named with your parameters

• direct_sample: The direct_sample function, if provided by the model

• gradient: The gradient function, if provided by the model

• properties: A list of properties of the model; see monty_model_properties(). Currently this contains:

  • has_gradient: the model can compute its gradient

  • has_direct_sample: the model can sample from parameters space

  • is_stochastic: the model will behave stochastically

  • has_parameter_groups: The model has separable parameter groups

Details

The model argument can be a list or environment (something indexable by $) and have elements:

• density: A function that will compute some probability density. It must take an argument representing a parameter vector (a numeric vector) and return a single value. This is the posterior probability density in Bayesian inference, but it could be anything really. Models can return -Inf if things are impossible, and we'll try and cope gracefully with that wherever possible. If the property allow_multiple_parameters is TRUE, then this function must be able to handle the argument parameter being a matrix, and return a vector of densities.

• parameters: A character vector of parameter names. This vector is the source of truth for the length of the parameter vector.

• domain: Information on the parameter domain. This is a two column matrix with length(parameters) rows representing each parameter. The parameter minimum and maximum bounds are given as the first and second column. Infinite values (-Inf or Inf) should be used where the parameter has infinite domain up or down. Currently used to translate from a bounded to unbounded space for HMC, but we might also use this for reflecting proposals in MCMC too, as well as a fast way of avoiding calculating densities where proposals fall out of bounds. If not present we assume that the model is valid everywhere (i.e., that all parameters are valid from -Inf to Inf. If unnamed, you must provide a domain for all parameters. If named, then you can provide a subset, with parameters that are not included assumed to have a domain of (-Inf, Inf).

• direct_sample: A function to sample directly from the parameter space, given a monty_rng object to sample from. In the case where a model returns a posterior (e.g., in Bayesian inference), this is assumed to be sampling from the prior. We'll use this for generating initial conditions for MCMC where those are not given, and possibly other uses. If not given then when using monty_sample() the user will have to provide a vector of initial states.

• gradient: A function to compute the gradient of density with respect to the parameter vector; takes a parameter vector and returns a vector the same length. For efficiency, the model may want to be stateful so that gradients can be efficiently calculated after a density calculation, or density after gradient, where these are called with the same parameters. This function is optional (and may not be well defined or possible to define).

• set_rng_state: A function to set the state (this is in contrast to the rng that is passed through to direct_sample as that is the sampler's rng stream, but we assume models will look after their own stream, and that they may need many streams). Models that provide this method are assumed to be stochastic; however, you can use the is_stochastic property (via monty_model_properties()) to override this (e.g., to run a stochastic model with its deterministic expectation). This function takes a raw vector of random number state from monty_rng and uses it to set the random number state for your model; this is derived from the random number stream for a particular chain, jumped ahead.

• get_rng_state: A function to get the RNG state; must be provided if set_rng_state is present. Must return the random number state, which is a raw vector (potentially quite long).

• parameter_groups: Optionally, an integer vector indicating parameter group membership. The format here may change (especially if we explore more complex nestings) but at present parameters with group 0 affect everything (so are accepted or rejected as a whole), while parameters in groups 1 to n are independent (for example, changing the parameters in group 2 does not affect the density of parameters proposed in group 3).