  Magnetic tunnel junctions (MTJ's) with low barrier magnets have been used to
implement random number generators (RNG's) and it has recently been shown that
such an MTJ connected to the drain of a conventional transistor provides a
three-terminal tunable RNG or a $p$-bit. In this letter we show how this
$p$-bit can be used to build a $p$-circuit that emulates a Bayesian network
(BN), such that the correlations in real world variables can be obtained from
electrical measurements on the corresponding circuit nodes. The $p$-circuit
design proceeds in two steps: the BN is first translated into a behavioral
model, called Probabilistic Spin Logic (PSL), defined by dimensionless biasing
(h) and interconnection (J) coefficients, which are then translated into
electronic circuit elements. As a benchmark example, we mimic a family tree of
three generations and show that the genetic relatedness calculated from a
SPICE-compatible circuit simulator matches well-known results.
