(iv) [true or false] With a deterministic transition model and a stochastic observation model, as time goes to infinity, when running a particle filter we will end up with all identical particles.

(v) [true or false] With a deterministic observation model, all particles might end up having zero weight. 

(vi) [true or false] It is possible to use particle filtering when the state space is continuous. 

(vii) [true or false] It is possible to use particle filtering when the state space is discrete. 

(viii) [true or false] As the number of particles goes to infinity, particle filtering will represent the same probability distribution that you’d get by using exact inference. 

(ix) [true or false] Particle filtering can represent a flat distribution (i.e. uniform) with fewer particles than it would need for a more concentrated distribution (i.e. Gaussian).