# randomprobs

##### Probabilities of random boards

Probabilities of a random Markov chain of boards, chosen by the Metropolis-Hastings algorithm

- Keywords
- array

##### Usage

```
randomprobs(x, B=2000, n=100, burnin = 0, use.brob=FALSE, func=NULL)
randomboards(x, B=2000, n=100, burnin=0)
candidate(x, n = 100, give = FALSE)
```

##### Arguments

- x
- Matrix, coerced to class
`board`

: the start point - B
- Number of samples to take
- burnin
- Number of samples to discard at the beginning
- use.brob
- Boolean, with default
`FALSE`

meaning to use IEEE arithmetic and`TRUE`

meaning to use Brobdingnagian arithmetic - n
- The number of times to try to find a candidate board with no non-negative entries; special value $0$ means to search until one is found
- func
- In function
`randomprobs()`

, the statistic to return; default of`NULL`

interpreted as`prob()`

- give
- In function
`candidate()`

, Boolean with default`FALSE`

meaning to return a permissible board, and`TRUE`

meaning to return instead the number of attempts made to find a permissible board (zero meaning no board

##### Value

- Function
`randomprobs()`

returns a vector of length`B`

with entries corresponding to the probabilities of the boards encountered.Function

`randomboards()`

returns an array with slices being successive boards

##### Note

Argument `n`

of function `candidate()`

specifies how many
times to search for a board with no non-negative entries. The special
value `n=0`

means to search until one is found.

Boards with a large number of zeros may require more than the default
100 attempts to find a permissible board. Set the `give`

flag to
see how many candidates are generated before a permissible one is found.

**Warning:** a board with at most one entry greater than zero is
the unique permissible board and the algorithm will not terminate if
`n=0`

A board that requires more than 100 attempts is probably well-suited
to the exact test as permissible boards will likely be enumerable
using `allboards()`

.

To find the permissible board that maximizes some objective function,
use `best()`

, which applies the bespoke optimization routines of
`optim()`

##### References

- N. A. Metropolis and others 1953.
*Equation of State Calculations by Fast Computing Machines*. Journal of Chemical Physics, 21:1087--1092

##### See Also

##### Examples

```
data(chess)
aylmer.test(chess)
a <- matrix(1,9,9) # See Sloane's A110058
plot(randomprobs(a,1000),type="b",main="Importance of burn-in")
set.seed(0)
b <- diag(rep(6,6))
plot(randomprobs(b,B=1000,n=1000), type="b",main="Importance of burn-in, part II")
data(purum)
randomboards(purum,10)
```

*Documentation reproduced from package aylmer, version 1.0-5, License: GPL-2*