Implementation of Bayesian Neural Networks

---

Documentation for package ‘BayesFluxR’ version 0.1.3

• DESCRIPTION file.
• Package NEWS.

Help Pages

.install_pkg	Installs Julia packages if needed
.julia_project_status	Obtain the status of the current Julia project
.set_seed	Set a seed both in Julia and R
.using	Loads Julia packages
BayesFluxR_setup	Set up of the Julia environment needed for BayesFlux
bayes_by_backprop	Use Bayes By Backprop to find Variational Approximation to BNN.
BNN	Create a Bayesian Neural Network
BNN.totparams	Obtain the total parameters of the BNN
Chain	Chain various layers together to form a network
Dense	Create a Dense layer with 'in_size' inputs and 'out_size' outputs using 'act' activation function
find_mode	Find the MAP of a BNN using SGD
Gamma	Create a Gamma Prior
get_random_symbol	Creates a random string that is used as variable in julia
initialise.allsame	Initialises all parameters of the network, all hyper parameters of the prior and all additional parameters of the likelihood by drawing random values from 'dist'.
InverseGamma	Create an Inverse-Gamma Prior
likelihood.feedforward_normal	Use a Normal likelihood for a Feedforward network
likelihood.feedforward_tdist	Use a t-Distribution likelihood for a Feedforward network
likelihood.seqtoone_normal	Use a Normal likelihood for a seq-to-one recurrent network
likelihood.seqtoone_tdist	Use a T-likelihood for a seq-to-one recurrent network.
LSTM	Create an LSTM layer with 'in_size' input size, and 'out_size' hidden state size
madapter.DiagCov	Use the diagonal of sample covariance matrix as inverse mass matrix.
madapter.FixedMassMatrix	Use a fixed mass matrix
madapter.FullCov	Use the full covariance matrix as inverse mass matrix
madapter.RMSProp	Use RMSProp to adapt the inverse mass matrix.
mcmc	Sample from a BNN using MCMC
Normal	Create a Normal Prior
opt.ADAM	ADAM optimiser
opt.Descent	Standard gradient descent
opt.RMSProp	RMSProp optimiser
posterior_predictive	Draw from the posterior predictive distribution
prior.gaussian	Use an isotropic Gaussian prior
prior.mixturescale	Scale Mixture of Gaussian Prior
prior_predictive	Sample from the prior predictive of a Bayesian Neural Network
RNN	Create a RNN layer with 'in_size' input, 'out_size' hidden state and 'act' activation function
sadapter.Const	Use a constant stepsize in mcmc
sadapter.DualAverage	Use Dual Averaging like in STAN to tune stepsize
sampler.AdaptiveMH	Adaptive Metropolis Hastings as introduced in
sampler.GGMC	Gradient Guided Monte Carlo
sampler.HMC	Standard Hamiltonian Monte Carlo (Hybrid Monte Carlo).
sampler.SGLD	Stochastic Gradient Langevin Dynamics as proposed in Welling, M., & Teh, Y. W. (n.d.). Bayesian Learning via Stochastic Gradient Langevin Dynamics. 8.
sampler.SGNHTS	Stochastic Gradient Nose-Hoover Thermostat as proposed in
summary.BNN	Print a summary of a BNN
tensor_embed_mat	Embed a matrix of timeseries into a tensor
to_bayesplot	Convert draws array to conform with 'bayesplot'
Truncated	Truncates a Distribution
vi.get_samples	Draw samples form a variational family.