diff --git a/src/emcee/moves/__init__.py b/src/emcee/moves/__init__.py
index 94670341..d99a6c03 100644
--- a/src/emcee/moves/__init__.py
+++ b/src/emcee/moves/__init__.py
@@ -2,6 +2,7 @@
 
 from .de import DEMove
 from .de_snooker import DESnookerMove
+from .dime import DIMEMove
 from .gaussian import GaussianMove
 from .kde import KDEMove
 from .mh import MHMove
@@ -20,4 +21,5 @@
     "KDEMove",
     "DEMove",
     "DESnookerMove",
+    "DIMEMove",
 ]
diff --git a/src/emcee/moves/dime.py b/src/emcee/moves/dime.py
new file mode 100644
index 00000000..13d96272
--- /dev/null
+++ b/src/emcee/moves/dime.py
@@ -0,0 +1,172 @@
+# -*- coding: utf-8 -*-
+
+import numpy as np
+
+try:
+    from scipy.special import logsumexp
+    from scipy.stats import multivariate_t
+except ImportError:
+    multivariate_t = None
+
+from .red_blue import RedBlueMove
+
+__all__ = ["DIMEMove"]
+
+
+def mvt_sample(df, mean, cov, size, random):
+    """Sample from multivariate t distribution
+
+    The results from random.multivariate_normal with non-identity covariance matrix are not reproducibel across OS architecture. Since scipy.stats.multivariate_t is based on numpy's multivariate_normal, the workaround is to provide this manually.
+    """
+
+    dim = len(mean)
+
+    # draw samples
+    snorm = random.randn(size, dim)
+    chi2 = random.chisquare(df, size) / df
+
+    # calculate sqrt of covariance
+    svd_cov = np.linalg.svd(cov * (df - 2) / df)
+    sqrt_cov = svd_cov[0] * np.sqrt(svd_cov[1]) @ svd_cov[2]
+
+    return mean + snorm @ sqrt_cov / np.sqrt(chi2)[:, None]
+
+
+class DIMEMove(RedBlueMove):
+    r"""A proposal using adaptive differential-independence mixture ensemble MCMC.
+
+    This is the `Differential-Independence Mixture Ensemble proposal` as developed in `Ensemble MCMC Sampling for DSGE Models <https://gregorboehl.com/live/dime_mcmc_boehl.pdf>`_.
+
+    Parameters
+    ----------
+    sigma : float, optional
+        standard deviation of the Gaussian used to stretch the proposal vector.
+    gamma : float, optional
+        mean stretch factor for the proposal vector. By default, it is :math:`2.38 / \sqrt{2\,\mathrm{ndim}}` as recommended by `ter Braak (2006) <http://www.stat.columbia.edu/~gelman/stuff_for_blog/cajo.pdf>`_.
+    aimh_prob : float, optional
+        probability to draw an adaptive independence Metropolis Hastings (AIMH) proposal. By default this is set to :math:`0.1`.
+    df_proposal_dist : float, optional
+        degrees of freedom of the multivariate t distribution used for AIMH proposals. Defaults to :math:`10`.
+    rho : float, optional
+        decay parameter for the aimh proposal mean and covariances. Defaults to :math:`0.999`.
+    """
+
+    def __init__(
+        self,
+        sigma=1.0e-5,
+        gamma=None,
+        aimh_prob=0.1,
+        df_proposal_dist=10,
+        rho=0.999,
+        **kwargs
+    ):
+        if multivariate_t is None:
+            raise ImportError(
+                "you need scipy.stats.multivariate_t and scipy.special.logsumexp to use the DIMEMove"
+            )
+
+        self.sigma = sigma
+        self.g0 = gamma
+        self.decay = rho
+        self.aimh_prob = aimh_prob
+        self.dft = df_proposal_dist
+
+        super(DIMEMove, self).__init__(**kwargs)
+
+    def setup(self, coords):
+        # set some sane defaults
+
+        nchain, npar = coords.shape
+
+        if self.g0 is None:
+            # pure MAGIC
+            self.g0 = 2.38 / np.sqrt(2 * npar)
+
+        if not hasattr(self, "prop_cov"):
+            # even more MAGIC
+            self.prop_cov = np.eye(npar)
+            self.prop_mean = np.zeros(npar)
+            self.accepted = np.ones(nchain, dtype=bool)
+            self.cumlweight = -np.inf
+        else:
+            # update AIMH proposal distribution
+            self.update_proposal_dist(coords)
+
+    def propose(self, model, state):
+        """Wrap original propose to get the some info on the current state"""
+
+        self.lprobs = state.log_prob
+        state, accepted = super(DIMEMove, self).propose(model, state)
+        self.accepted = accepted
+        return state, accepted
+
+    def update_proposal_dist(self, x):
+        """Update proposal distribution with ensemble `x`"""
+
+        nchain, npar = x.shape
+
+        # log weight of current ensemble
+        if self.accepted.any():
+            lweight = (
+                logsumexp(self.lprobs)
+                + np.log(sum(self.accepted))
+                - np.log(nchain)
+            )
+        else:
+            lweight = -np.inf
+
+        # calculate stats for current ensemble
+        ncov = np.cov(x.T, ddof=1)
+        nmean = np.mean(x, axis=0)
+
+        # update AIMH proposal distribution
+        newcumlweight = np.logaddexp(self.cumlweight, lweight)
+        self.prop_cov = (
+            np.exp(self.cumlweight - newcumlweight) * self.prop_cov
+            + np.exp(lweight - newcumlweight) * ncov
+        )
+        self.prop_mean = (
+            np.exp(self.cumlweight - newcumlweight) * self.prop_mean
+            + np.exp(lweight - newcumlweight) * nmean
+        )
+        self.cumlweight = newcumlweight + np.log(self.decay)
+
+    def get_proposal(self, x, xref, random):
+        """Actual proposal function"""
+
+        xref = xref[0]
+        nchain, npar = x.shape
+        nref, _ = xref.shape
+
+        # differential evolution: draw the indices of the complementary chains
+        i0 = np.arange(nchain) + random.randint(1, nchain, size=nchain)
+        i1 = np.arange(nchain) + random.randint(1, nchain - 1, size=nchain)
+        i1 += i1 >= i0
+        # add small noise and calculate proposal
+        f = self.sigma * random.randn(nchain)
+        q = x + self.g0 * (xref[i0 % nref] - xref[i1 % nref]) + f[:, None]
+        factors = np.zeros(nchain, dtype=np.float64)
+
+        # draw chains for AIMH sampling
+        xchnge = random.rand(nchain) <= self.aimh_prob
+
+        # draw alternative candidates and calculate their proposal density
+        xcand = mvt_sample(
+            df=self.dft,
+            mean=self.prop_mean,
+            cov=self.prop_cov,
+            size=sum(xchnge),
+            random=random,
+        )
+        lprop_old, lprop_new = multivariate_t.logpdf(
+            np.vstack((x[None, xchnge], xcand[None])),
+            self.prop_mean,
+            self.prop_cov * (self.dft - 2) / self.dft,
+            df=self.dft,
+        )
+
+        # update proposals and factors
+        q[xchnge, :] = np.reshape(xcand, (-1, npar))
+        factors[xchnge] = lprop_old - lprop_new
+
+        return q, factors
diff --git a/src/emcee/tests/integration/test_dime.py b/src/emcee/tests/integration/test_dime.py
new file mode 100644
index 00000000..383efe71
--- /dev/null
+++ b/src/emcee/tests/integration/test_dime.py
@@ -0,0 +1,23 @@
+# -*- coding: utf-8 -*-
+
+try:
+    import scipy
+except ImportError:
+    scipy = None
+import pytest
+
+from emcee import moves
+
+from .test_proposal import _test_normal, _test_uniform
+
+__all__ = ["test_normal_dime", "test_uniform_de"]
+
+
+@pytest.mark.skipif(scipy is None, reason="scipy is not available")
+def test_normal_dime(**kwargs):
+    _test_normal(moves.DIMEMove(), **kwargs)
+
+
+@pytest.mark.skipif(scipy is None, reason="scipy is not available")
+def test_uniform_dime(**kwargs):
+    _test_uniform(moves.DIMEMove(), **kwargs)