  We study the probabilistic degree over reals of the OR function on $n$
variables. For an error parameter $\epsilon$ in (0,1/3), the $\epsilon$-error
probabilistic degree of any Boolean function $f$ over reals is the smallest
non-negative integer $d$ such that the following holds: there exists a
distribution $D$ of polynomials entirely supported on polynomials of degree at
most $d$ such that for all $z \in \{0,1\}^n$, we have $Pr_{P \sim D} [P(z) =
f(z) ] \geq 1- \epsilon$. It is known from the works of Tarui ({Theoret.
Comput. Sci.} 1993) and Beigel, Reingold, and Spielman ({ Proc. 6th CCC} 1991),
that the $\epsilon$-error probabilistic degree of the OR function is at most
$O(\log n.\log 1/\epsilon)$. Our first observation is that this can be improved
to $O{\log {{n}\choose{\leq \log 1/\epsilon}}}$, which is better for small
values of $\epsilon$.
  In all known constructions of probabilistic polynomials for the OR function
(including the above improvement), the polynomials $P$ in the support of the
distribution $D$ have the following special structure:$P = 1 -
(1-L_1).(1-L_2)...(1-L_t)$, where each $L_i(x_1,..., x_n)$ is a linear form in
the variables $x_1,...,x_n$, i.e., the polynomial $1-P(x_1,...,x_n)$ is a
product of affine forms. We show that the $\epsilon$-error probabilistic degree
of OR when restricted to polynomials of the above form is $\Omega ( \log
a/\log^2 a )$ where $a = \log {{n}\choose{\leq \log 1/\epsilon}}$. Thus
matching the above upper bound (up to poly-logarithmic factors).
