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Metropolis-Hastings sample

```
smpl = mhsample(start,nsamples,'pdf',pdf,'proppdf',proppdf,
'proprnd',proprnd)
```

smpl = mhsample(...,'symmetric',sym)

smpl = mhsample(...,'burnin',K)

smpl = mhsample(...,'thin',m)

smpl = mhsample(...,'nchain',n)

[smpl,accept] = mhsample(...)

```
smpl = mhsample(start,nsamples,'pdf',pdf,'proppdf',proppdf,
'proprnd',proprnd)
```

draws `nsamples`

random
samples from a target stationary distribution `pdf`

using
the Metropolis-Hastings algorithm.

`start`

is a row vector containing the start
value of the Markov Chain, `nsamples`

is an integer
specifying the number of samples to be generated, and `pdf`

, `proppdf`

,
and `proprnd`

are function handles created using `@`

. `proppdf`

defines
the proposal distribution density, and `proprnd`

defines
the random number generator for the proposal distribution. `pdf`

and `proprnd`

take
one argument as an input with the same type and size as `start`

. `proppdf`

takes
two arguments as inputs with the same type and size as `start`

.

`smpl`

is a column vector or matrix containing
the samples. If the log density function is preferred, `'pdf'`

and `'proppdf'`

can
be replaced with `'logpdf'`

and `'logproppdf'`

.
The density functions used in Metropolis-Hastings algorithm are not
necessarily normalized.

The proposal distribution * q*(

If the `proppdf`

or `logproppdf`

satisfies * q*(

`mhsample`

implements
Random Walk Metropolis-Hastings sampling. If the `proppdf`

or `logproppdf`

satisfies `mhsample`

implements
Independent Metropolis-Hastings sampling.`smpl = mhsample(...,'symmetric',sym)`

draws `nsamples`

random
samples from a target stationary distribution `pdf`

using
the Metropolis-Hastings algorithm. `sym`

is a logical
value that indicates whether the proposal distribution is symmetric.
The default value is false, which corresponds to the asymmetric proposal
distribution. If `sym`

is true, for example, the
proposal distribution is symmetric, `proppdf`

and `logproppdf`

are
optional.

`smpl = mhsample(...,'burnin',K)`

generates
a Markov chain with values between the starting point and the `k`

^{th} point
omitted in the generated sequence. Values beyond the `k`

^{th} point
are kept. `k`

is a nonnegative integer with default
value of `0`

.

`smpl = mhsample(...,'thin',m)`

generates
a Markov chain with `m-1`

out of `m`

values
omitted in the generated sequence. `m`

is a positive
integer with default value of `1`

.

`smpl = mhsample(...,'nchain',n)`

generates `n`

Markov
chains using the Metropolis-Hastings algorithm. `n`

is
a positive integer with a default value of 1. `smpl`

is
a matrix containing the samples. The last dimension contains the indices
for individual chains.

`[smpl,accept] = mhsample(...)`

also returns `accept`

,
the acceptance rate of the proposed distribution. `accept`

is
a scalar if a single chain is generated and is a vector if multiple
chains are generated.

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