burnin {LaplacesDemon} | R Documentation |

## Burn-in

### Description

The `burnin`

function estimates the duration of burn-in in
iterations for one or more Markov chains. “Burn-in” refers to the
initial portion of a Markov chain that is not stationary and is still
affected by its initial value.

### Usage

```
burnin(x, method="BMK")
```

### Arguments

`x` |
This is a vector or matrix of posterior samples for which a the number of burn-in iterations will be estimated. |

`method` |
This argument defaults to |

### Details

Burn-in is a colloquial term for the initial iterations in a Markov chain prior to its convergence to the target distribution. During burn-in, the chain is not considered to have “forgotten” its initial value.

Burn-in is not a theoretical part of MCMC, but its use is the norm because of the need to limit the number of posterior samples due to computer memory. If burn-in were retained rather than discarded, then more posterior samples would have to be retained. If a Markov chain starts anywhere close to the center of its target distribution, then burn-in iterations do not need to be discarded.

In the `LaplacesDemon`

function, stationarity is estimated
with the `BMK.Diagnostic`

function on all thinned
posterior samples of each chain, beginning at cumulative 10% intervals
relative to the total number of samples, and the lowest number in
which all chains are stationary is considered the burn-in.

The term, “burn-in”, originated in electronics regarding the initial testing of component failure at the factory to eliminate initial failures (Geyer, 2011). Although “burn-in' has been the standard term for decades, some are referring to these as “warm-up” iterations.

### Value

The `burnin`

function returns a vector equal in length to the
number of MCMC chains in `x`

, and each element indicates the
maximum iteration in burn-in.

### Author(s)

Statisticat, LLC. software@bayesian-inference.com

### References

Geyer, C.J. (2011). "Introduction to Markov Chain Monte Carlo". In S Brooks, A Gelman, G Jones, and M Xiao-Li (eds.), "Handbook of Markov Chain Monte Carlo", p. 3–48. Chapman and Hall, Boca Raton, FL.

### See Also

`BMK.Diagnostic`

,
`deburn`

,
`Geweke.Diagnostic`

,
`KS.Diagnostic`

, and
`LaplacesDemon`

.

### Examples

```
library(LaplacesDemon)
x <- rnorm(1000)
burnin(x)
```

*LaplacesDemon*version 16.1.6 Index]