diff --git a/python/ray/tune/suggest/bayesopt.py b/python/ray/tune/suggest/bayesopt.py
index 1e743f653..37fa5a846 100644
--- a/python/ray/tune/suggest/bayesopt.py
+++ b/python/ray/tune/suggest/bayesopt.py
@@ -38,6 +38,8 @@ class BayesOptSearch(Searcher):
             - kappa: 2.576
             - xi: 0.0
         random_state (int): Used to initialize BayesOpt.
+        analysis (ExperimentAnalysis): Optionally, the previous analysis
+            to integrate.
         verbose (int): Sets verbosity level for BayesOpt packages.
         max_concurrent: Deprecated.
         use_early_stopped_trials: Deprecated.
@@ -64,8 +66,27 @@ class BayesOptSearch(Searcher):
                  utility_kwargs=None,
                  random_state=1,
                  verbose=0,
+                 analysis=None,
                  max_concurrent=None,
                  use_early_stopped_trials=None):
+        """Instantiate new BayesOptSearch object.
+
+        Parameters:
+            space (dict): Continuous search space.
+                Parameters will be sampled from
+                this space which will be used to run trials.
+            metric (str): The training result objective value attribute.
+            mode (str): One of {min, max}. Determines whether objective is
+                minimizing or maximizing the metric attribute.
+            utility_kwargs (dict): Parameters to define the utility function.
+                Must provide values for the keys `kind`, `kappa`, and `xi`.
+            random_state (int): Used to initialize BayesOpt.
+            analysis (ExperimentAnalysis): Optionally, the previous analysis
+                to integrate.
+            verbose (int): Sets verbosity level for BayesOpt packages.
+            max_concurrent: Deprecated.
+            use_early_stopped_trials: Deprecated.
+        """
         assert byo is not None, (
             "BayesOpt must be installed!. You can install BayesOpt with"
             " the command: `pip install bayesian-optimization`.")
@@ -96,6 +117,8 @@ class BayesOptSearch(Searcher):
             f=None, pbounds=space, verbose=verbose, random_state=random_state)
 
         self.utility = byo.UtilityFunction(**utility_kwargs)
+        if analysis is not None:
+            self.register_analysis(analysis)
 
     def suggest(self, trial_id):
         if self.max_concurrent:
@@ -107,6 +130,21 @@ class BayesOptSearch(Searcher):
 
         return copy.deepcopy(new_trial)
 
+    def register_analysis(self, analysis):
+        """Integrate the given analysis into the gaussian process.
+
+        Parameters
+        ------------------
+        analysis (ExperimentAnalysis): Optionally, the previous analysis
+            to integrate.
+        """
+        for (_, report), params in zip(analysis.dataframe().iterrows(),
+                                       analysis.get_all_configs().values()):
+            # We add the obtained results to the
+            # gaussian process optimizer
+            self.optimizer.register(
+                params=params, target=self._metric_op * report[self._metric])
+
     def on_trial_complete(self, trial_id, result=None, error=False):
         """Notification for the completion of trial."""
         if result:
diff --git a/python/ray/tune/tests/test_tune_restore.py b/python/ray/tune/tests/test_tune_restore.py
index ae0413a99..5de3af43a 100644
--- a/python/ray/tune/tests/test_tune_restore.py
+++ b/python/ray/tune/tests/test_tune_restore.py
@@ -202,19 +202,22 @@ class HyperoptWarmStartTest(AbstractWarmStartTest, unittest.TestCase):
 
 
 class BayesoptWarmStartTest(AbstractWarmStartTest, unittest.TestCase):
-    def set_basic_conf(self):
+    def set_basic_conf(self, analysis=None):
         space = {"width": (0, 20), "height": (-100, 100)}
 
         def cost(space, reporter):
             reporter(loss=(space["height"] - 14)**2 - abs(space["width"] - 3))
 
         search_alg = BayesOptSearch(
-            space,
-            metric="loss",
-            mode="min",
-        )
+            space, metric="loss", mode="min", analysis=analysis)
         return search_alg, cost
 
+    def testBootStrapAnalysis(self):
+        analysis = self.run_exp_3()
+        search_alg3, cost = self.set_basic_conf(analysis)
+        search_alg3 = ConcurrencyLimiter(search_alg3, 1)
+        tune.run(cost, num_samples=10, search_alg=search_alg3, verbose=0)
+
 
 class SkoptWarmStartTest(AbstractWarmStartTest, unittest.TestCase):
     def set_basic_conf(self):