Fix docstrings

doc/demos/inference/inference.rst

@@ -676,9 +676,9 @@ will output:
     niter:    53  count:    9  breaks:  1  min_S: 702.85027  max_S: 761.09434  S: 735.06773  ΔS:      24.1310  moves:    28 
     niter:    54  count:   10  breaks:  2  min_S: 702.85027  max_S: 761.09434  S: 738.16756  ΔS:      3.09983  moves:   115
 
-Note that the value of `wait` above was made purposefully low so that
+Note that the value of ``wait`` above was made purposefully low so that
 the output would not be overly long. The most appropriate value requires
-experimentation, but a typically good value is `wait=1000`.
+experimentation, but a typically good value is ``wait=1000``.
 
 The function :func:`~graph_tool.inference.mcmc_equilibrate` accepts a
 ``callback`` argument that takes an optional function to be invoked
@@ -767,7 +767,7 @@ analogous, but now we make use of
 
     When using :class:`~graph_tool.inference.NestedBlockState` instances
     to perform model averaging, they need to be constructed with the
-    option `sampling=True`.
+    option ``sampling=True``.
 
 Here we perform the sampling of hierarchical partitions using the same
 network as above.

src/graph_tool/inference/blockmodel.py

@@ -1658,7 +1658,7 @@ class BlockState(object):
            ...     ds, nmoves = state.mcmc_sweep(niter=10)
            ...     pe = state.collect_edge_marginals(pe)
            >>> gt.bethe_entropy(g, pe)[0]
-           6.65429696440...
+           -59.2541131...
         """
 
         if p is None:
@@ -1712,7 +1712,7 @@ class BlockState(object):
            ...     ds, nmoves = state.mcmc_sweep(niter=10)
            ...     pv = state.collect_vertex_marginals(pv)
            >>> gt.mf_entropy(g, pv)
-           29.84318996...
+           25.98257...
            >>> gt.graph_draw(g, pos=g.vp["pos"], vertex_shape="pie",
            ...               vertex_pie_fractions=pv, output="polbooks_blocks_soft_B4.pdf")
            <...>
@@ -1778,7 +1778,7 @@ class BlockState(object):
            ...     ds, nmoves = state.mcmc_sweep(niter=10)
            ...     ph = state.collect_partition_histogram(ph)
            >>> gt.microstate_entropy(ph)
-           124.092176...
+           128.055896...
 
         """