diff --git a/high_dimensional_sampling/posterior/dynastysampler.py b/high_dimensional_sampling/posterior/dynastysampler.py
new file mode 100644
index 0000000..58c3a8d
--- /dev/null
+++ b/high_dimensional_sampling/posterior/dynastysampler.py
@@ -0,0 +1,168 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Thu May 12 15:40:38 2022
+
+@author: dm
+"""
+
+import high_dimensional_sampling as hds
+
+# basic numeric setup
+import numpy as np
+from numpy import linalg
+from importlib import import_module
+"""
+Example of an optimisation experiment. Implemented procedure is explained at
+https://en.wikipedia.org/wiki/Random_optimization
+"""
+"""
+Dynesty usage:
+    The sampler recieves 3 arguments: loglike,ptform,ndim
+    where loglike is the log-likelihood function, ptform is the prior
+    transform, and ndim the dimensionality of the problem.
+    
+    Dynesty sampler is documented here:
+    https://dynesty.readthedocs.io/en/latest/api.html?highlight=dynesty.NestedSampler#dynesty.dynesty.NestedSampler
+    
+
+"""
+
+
+class DynestySampler(hds.Procedure):
+    def __init__(self,
+                 n_initial=10,
+                 n_sample=10,
+                 converge: bool = False,
+                 **kwargs):
+        self.store_parameters = ['n_initial', 'n_sample']
+        self.n_initial = n_initial
+        self.n_sample = n_sample
+        self.dynasty = import_module("dynasty")
+        self.sampler = self.dynesty.NestedSampler(
+            loglikelihood=self.__DefaultLogLike__,
+            prior_transform=self.__Default_prior_transform,
+            ndim=3)  #TBD
+        self.reset()
+        self._rstate = np.random.default_rng(5647)
+        self._res = None
+        self._function = 0
+        self._converge = converge
+        self._kwargs = {
+            'bound': None,
+            'method': None,
+            'update_interval_ratio': None,
+            'first_update': None,
+            'rstate': None,
+            'queue_size': None,
+            'pool': None,
+            'use_pool': None,
+            'ncdim': None,
+            'nlive0': None,
+            'kwargs': None
+        }
+        self._comstring = ''
+        for key in kwargs:
+            try:
+                self._kwargs[key] = kwargs[key]
+                self._comstring += ',' + str(key) + '=' + str(kwargs[key])
+            except Exception as e:
+                print(
+                    "Exception: most probably you didn't use a correct keyword in **kwargs"
+                )
+                raise e
+
+    def __DefaultLogLike__(self, x):
+        ndim = 3  # number of dimensions
+        C = np.identity(ndim)  # set covariance to identity matrix
+        C[C == 0] = 0.95  # set off-diagonal terms (strongly correlated)
+        Cinv = linalg.inv(C)  # precision matrix
+        lnorm = -0.5 * (np.log(2 * np.pi) * ndim + np.log(linalg.det(C))
+                        )  # ln(normalization)
+
+        # 3-D correlated multivariate normal log-likelihood
+
+        return -0.5 * np.dot(x, np.dot(Cinv, x)) + lnorm
+
+    def __Default_prior_transform(self, u):
+        """Transforms our unit cube samples `u` to a flat prior between -10. and 10. in each variable."""
+        return 1. * (2. * u - 1.)
+
+    def prior_transform(self, u):
+        return (u * self.ranges + self.mins)
+
+    def _CreateSampler(self, function):
+        minmax = np.array(function.get_ranges(0.01))
+        self.ranges = minmax[:, 1] - minmax[:, 0]
+        self.mins = minmax[:, 0]
+        command = """self.sampler = self.dynesty.NestedSampler(loglikelihood=function,prior_transform=self.prior_transform,ndim=function.get_dimensionality(),nlive=self.n_sample"""
+        command += self._comstring + ')'
+        print(command)
+        exec(command)
+        self._function = function
+        print(self.sampler.bound)
+
+    def __call__(self, function):
+        """
+            Here we first create import the test function into the sampler,
+        """
+        if self._function != function:
+            self._CreateSampler(function)
+        try:
+            self.sampler.run_nested(maxiter=1, maxcall=1, print_progress=False)
+            self._res = self.sampler.results
+
+        except Exception as e:
+            print('exeption occured')
+            pass
+        #self._FirstCall=True
+
+        # Get ranges of the test function. The 0.001 moves the minima 0.001 up
+        # and the maxima 0.001 down, in order to make use the sampling is not
+        # by accident moving outside of the test function range.
+
+        return (self._res['samples'], self._res['logz'])
+
+    def get_initial_position(self, ranges, n_sample_initial):
+        ndim = len(ranges)
+        r = np.array(ranges)
+        x = np.random.rand(n_sample_initial, ndim)
+        x = x * (r[:, 1] - r[:, 0]) + r[:, 0]
+        return x
+
+    def get_point(self, ranges, stdev=0.01, n_sample=1):
+        cov = np.identity(len(ranges)) * stdev
+        return np.random.multivariate_normal(self.current_position[0], cov,
+                                             n_sample)
+
+    def reset(self):
+        self.current_position = None
+        self.current_value = None
+
+    def is_finished(self):
+
+        return (self._converge
+                and self.dynesty.dynamicsampler.stopping_function(
+                    self.sampler.results))
+        #return False
+
+    def check_testfunction(self, function):
+        return True
+
+
+"""
+ Usage example
+
+"""
+kwargs = {'bound': '"balls"'}
+
+procedure = DynestySampler(n_initial=1000,
+                           n_sample=1000,
+                           converge=True,
+                           **kwargs)
+experiment = hds.OptimisationExperiment(procedure, './hds')
+feeder = hds.functions.FunctionFeeder()
+feeder.load_function_group(['optimisation', 'bounded'])
+
+for function in feeder:
+    experiment.run(function, finish_line=10000000)