<s>
In	O
statistics	O
,	O
the	O
antithetic	B-Algorithm
variates	I-Algorithm
method	O
is	O
a	O
variance	B-Algorithm
reduction	I-Algorithm
technique	O
used	O
in	O
Monte	B-Algorithm
Carlo	I-Algorithm
methods	I-Algorithm
.	O
</s>
<s>
Considering	O
that	O
the	O
error	O
in	O
the	O
simulated	O
signal	O
(	O
using	O
Monte	B-Algorithm
Carlo	I-Algorithm
methods	I-Algorithm
)	O
has	O
a	O
one-over	O
square	O
root	O
convergence	B-Algorithm
,	O
a	O
very	O
large	O
number	O
of	O
sample	O
paths	O
is	O
required	O
to	O
obtain	O
an	O
accurate	O
result	O
.	O
</s>
<s>
The	O
antithetic	B-Algorithm
variates	I-Algorithm
method	O
reduces	O
the	O
variance	O
of	O
the	O
simulation	O
results	O
.	O
</s>
<s>
The	O
antithetic	B-Algorithm
variates	I-Algorithm
technique	O
consists	O
,	O
for	O
every	O
sample	O
path	O
obtained	O
,	O
in	O
taking	O
its	O
antithetic	O
path	O
that	O
is	O
given	O
a	O
path	O
to	O
also	O
take	O
.	O
</s>
<s>
Furthermore	O
,	O
covariance	O
is	O
negative	O
,	O
allowing	O
for	O
initial	O
variance	B-Algorithm
reduction	I-Algorithm
.	O
</s>
<s>
The	O
following	O
table	O
compares	O
the	O
classical	O
Monte	O
Carlo	O
estimate	O
(	O
sample	O
size	O
:	O
2n	O
,	O
where	O
n	O
=	O
1500	O
)	O
to	O
the	O
antithetic	B-Algorithm
variates	I-Algorithm
estimate	O
(	O
sample	O
size	O
:	O
n	O
,	O
completed	O
with	O
the	O
transformed	O
sample	O
1ui	O
)	O
:	O
</s>
<s>
The	O
use	O
of	O
the	O
antithetic	B-Algorithm
variates	I-Algorithm
method	O
to	O
estimate	O
the	O
result	O
shows	O
an	O
important	O
variance	B-Algorithm
reduction	I-Algorithm
.	O
</s>
