madapter.DiagCov {BayesFluxR} | R Documentation |

## Use the diagonal of sample covariance matrix as inverse mass matrix.

### Description

Use the diagonal of sample covariance matrix as inverse mass matrix.

### Usage

```
madapter.DiagCov(adapt_steps, windowlength, kappa = 0.5, epsilon = 1e-06)
```

### Arguments

`adapt_steps` |
Number of adaptation steps |

`windowlength` |
Lookback window length for calculation of covariance |

`kappa` |
How much to shrink towards the identity |

`epsilon` |
Small value to add to diagonal so as to avoid numerical non-pos-def problem |

### Value

list containing 'juliavar' and 'juliacode' and all given arguments.

### Examples

```
## Not run:
## Needs previous call to `BayesFluxR_setup` which is time
## consuming and requires Julia and BayesFlux.jl
BayesFluxR_setup(installJulia=TRUE, seed=123)
net <- Chain(Dense(5, 1))
like <- likelihood.feedforward_normal(net, Gamma(2.0, 0.5))
prior <- prior.gaussian(net, 0.5)
init <- initialise.allsame(Normal(0, 0.5), like, prior)
x <- matrix(rnorm(5*100), nrow = 5)
y <- rnorm(100)
bnn <- BNN(x, y, like, prior, init)
madapter <- madapter.DiagCov(100, 10)
sampler <- sampler.GGMC(madapter = madapter)
ch <- mcmc(bnn, 10, 1000, sampler)
## End(Not run)
```

[Package

*BayesFluxR*version 0.1.3 Index]