Large-scale particle data sets, such as those computed in molecular dynamics (MD) simulations, are crucial to investigating important processes in physics and thermodynamics. The simulated atoms are usually visualized as hard spheres with Phong shading, where individual particles can be perceived well in close-up views. However, for large-scale simulations with millions of particles, the visualization of large fields-of-view usually suffers from strong aliasing artifacts, because the mismatch between data size and output resolution leads to severe under-sampling of the geometry. In this dissertation, we present novel visualization methods for large-scale particle data that address aliasing while enabling interactive high-quality rendering by sampling only the visible particles of a data set from a given view. The first contribution of this thesis is the novel concept of screen-space normal distribution functions (S-NDFs) for particle data. S-NDFs represent the distribution of surface normals that map to a given pixel in screen space, which enables high-quality re-lighting without re-rendering particles. In order to facilitate interactive zooming, we cache S-NDFs in a screen-space mipmap (S-MIP). Together, these two concepts enable interactive, scaleconsistent re-lighting and shading changes, as well as zooming, without having to re-sample the particle data. Our second contribution is a novel architecture for probabilistic culling of large particle data. Wedecouplethesuper-samplingforrenderingfromthedeterminationofsub-pixelparticle visibility, and perform culling probabilistically in multiple stages, while incrementally tracking confidence in the visibility data gathered so far to avoid wrong visibility decisions with high probability. Our architecture determines particle visibility with high accuracy, while only sampling a small part of the whole data set. The particles that are not occluded are then super-sampled for high rendering quality, at a fraction of the cost of sampling the entire data set.