dynamics-module

dynamics

iterate_lambda

iterate_state

iterate_lambda_vectorised

a square, two-dimensional (i.e. matrix-like) greta array
representing transition probabilities between states

matrix

a column vector greta array representing the initial state from
which to iterate the matrix

state

a positive integer giving the number of times to iterate the
matrix

niter

a rectangular two-dimensional greta array of dimension n x
m^2, each row of which gives the rowwise elements of a different m x m
matrix to iterate

matrices

the number of m x m matrices to be iterated (first dimensions of
<code>matrices</code>)

the dimension of each matrix to be iterated

A module providing functions specific to dynamical modelling. So
  far only for iterating Leslie matrices. <code>iterate_lambda</code> iterates a
  matrix a certain number of times and returns, as a scalar greta array, the
  terminal growth rate for the first element of the state vector.
  <code>iterate_state</code> carries out the same procedure, but returns the final
  state vector. <code>iterate_lambda_vectorised</code> is a vectorised version of
  <code>iterate_lambda</code> for iterating over multiple matrices, returning a
  vector of growth rates.

Existing tools for fitting bespoke statistical models (such as
BUGS, JAGS and STAN) are very effective for moderately-sized problems, but
don't scale so well to large datasets. These tools also require users to learn
a domain-specific language and fix errors at compile time. greta enables users
to construct probabilistic models interactively in native R code, then sample
from those models efficiently using Hamiltonian Monte Carlo. TensorFlow is used
to perform all of the calculations, so greta is particularly fast where the
model contains large linear algebra operations. greta can also be run across
distributed machines or on GPUs, just by installing the relevant version of
TensorFlow. This package is in the early stages of development. Future releases
will likely enable fitting models with fast approximate inference schemes,
different samplers, and more distributions and operations.

Nick Golding

greta

Probabilistic Modelling with TensorFlow

dynamics-module function

<p></p><p><pre>
  dynamics$iterate_lambda(matrix, state, niter)
  dynamics$iterate_state(matrix, state, niter)
  dynamics$iterate_lambda_vectorised(matrices, state, n, m, niter)
</pre></p>

dynamics-module: Functions for modelling dynamical systems

Description

Arguments

Usage