<s>
Spike-and-slab	B-General_Concept
regression	I-General_Concept
is	O
a	O
type	O
of	O
Bayesian	B-General_Concept
linear	I-General_Concept
regression	I-General_Concept
in	O
which	O
a	O
particular	O
hierarchical	O
prior	O
distribution	O
for	O
the	O
regression	O
coefficients	O
is	O
chosen	O
such	O
that	O
only	O
a	O
subset	B-General_Concept
of	I-General_Concept
the	I-General_Concept
possible	I-General_Concept
regressors	I-General_Concept
is	O
retained	O
.	O
</s>
<s>
A	O
common	O
choice	O
on	O
that	O
step	O
is	O
to	O
use	O
a	O
Normal	O
prior	O
with	O
mean	O
equal	O
to	O
zero	O
and	O
a	O
large	O
variance	O
calculated	O
based	O
on	O
(	O
where	O
is	O
a	O
design	B-Algorithm
matrix	I-Algorithm
of	O
explanatory	O
variables	O
of	O
the	O
model	O
)	O
.	O
</s>
<s>
All	O
steps	O
of	O
the	O
described	O
algorithm	O
are	O
repeated	O
thousands	O
of	O
times	O
using	O
Markov	B-General_Concept
chain	I-General_Concept
Monte	I-General_Concept
Carlo	I-General_Concept
(	O
MCMC	O
)	O
technique	O
.	O
</s>
<s>
An	O
advantage	O
of	O
Bayesian	O
variable	B-General_Concept
selection	I-General_Concept
techniques	O
is	O
that	O
they	O
are	O
able	O
to	O
make	O
use	O
of	O
prior	O
knowledge	O
about	O
the	O
model	O
.	O
</s>
