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This paper considers Bayesian data fusion of conventional robot sensor information with ambiguous human-generated categorical information about continuous world states of interest. First, it is shown that such soft information can be generally modeled via hybrid continuous-to-discrete likelihoods that are based on the softmax function. A new hybrid fusion procedure, called variational Bayesian importance sampling (VBIS), is then introduced to combine the strengths of variational Bayes approximations and fast Monte Carlo methods to produce reliable posterior estimates for Gaussian priors and softmax likelihoods. VBIS is then extended to more general fusion problems that involve complex Gaussian mixture (GM) priors and multimodal softmax likelihoods, leading to accurate GM approximations of highly non-Gaussian fusion posteriors for a wide range of robot sensor data and soft human data. Experiments for hardware-based multitarget search missions with a cooperative human-autonomous robot team show that humans can serve as highly informative sensors through proper data modeling and fusion, and that VBIS provides reliable and scalable Bayesian fusion estimates via GMs.