Man pages for enweg/BFluxR
Implementation of Bayesian Neural Networks
| bayes_by_backprop | Use Bayes By Backprop to find Variational Approximation to... |
| BayesFluxR-package | BayesFluxR: Implementation of Bayesian Neural Networks |
| BayesFluxR_setup | Set up of the Julia environment needed for BayesFlux |
| 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'... |
| dot-install_pkg | Installs Julia packages if needed |
| dot-julia_project_status | Obtain the status of the current Julia project |
| dot-set_seed | Set a seed both in Julia and R |
| dot-using | Loads Julia packages |
| 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... |
| 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... |
| madapter.DiagCov | Use the diagonal of sample covariance matrix as inverse mass... |
| 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... |
| 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,... |
| 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. |
