  This paper analyses the impact of various parameter changes on the estimation
of parameters for massive black hole binary (MBHB) systems using a Bayesian
inference technique. Several designed MBHB systems were chosen for comparison
with a fiducial system to explore the influence of parameters such as sky
location, inclination angle, anti-spin, large mass ratio and light mass. And
the two reported MBHB candidates named OJ287 and Tick-Tock are also considered.
The study found that the network of TianQin and LISA can break certain
degeneracies among different parameters, improving the estimation of
parameters, particularly for extrinsic parameters. Meanwhile, the degeneracies
between different intrinsic parameters are highly sensitive to the value of the
parameters. Additionally, the small inclination angles and limited detection of
the inspiral phase can introduce significant bias in the estimation of
parameters. The presence of instrument noise will also introduce bias and
worsen the precision. The paper concludes that the network of TianQin and LISA
can significantly improve the estimation of extrinsic parameters by about one
order of magnitude while yielding slight improvements in the intrinsic
parameters. Moreover, parameter estimation can still be subject to biases even
with a sufficiently high signal-to-noise ratio if the detected signal does not
encompass all stages of the inspiral, merger, and ringdown.
