Many of the problems that a typical brain is required to solve are of essentially Bayesian nature. How - or if at all - a Bayesian algorithm is embedded in the structure and dynamics of cortical neural networks remains the subject of considerable debate. Experimental evidence of a "Bayesian brain" has been steadily growing, as has the collection of models that have been put forward as possible candidates for a neuronal implementation of Bayesian inference. The study of generative and discriminative models for various types of data is further bolstered by advances in fields that lie outside of neuroscience, but have obvious conceptual connections, such as machine learning and AI. Recent theoretical studies point towards a link between these models and biological neural networks. Whether biological or not, all of these models are only as efficient as the physical substrate that they are embedded in allows them to be. Power efficiency as well as execution speed are of increasing importance as both the network models and their associated learning algorithms become large and/or complex. In our demo session, we will show the first implementation of neural sampling in mixed-signal, low-power and highly accelerated neuromorphic hardware. We will discuss the network structure and mechanisms by which we were able to counteract the imperfections that are inherent to the hardware manufacturing process and provide an interactive interface for users to manipulate the emulation parameters, in particular those of the target probability distribution from which the neuromorphic chip samples. The corresponding parameters of the on-chip spiking neural network can then either be calculated analytically or trained with simple learning rules. More advanced users which are familiar with spiking network simulators will be able to make use of the full versatility of the hardware substrate and program their own network models for neuromorphic emulation.