What is the meaning of Model(s, a) in the prioritized sweeping algorithm? I'm reading the book "Reinforcement Learning: An Introduction" (by Andrew Barto and Richard S. Sutton). 

The authors provide the pseudocode of the prioritized sweeping algorithm, but I do not know what is the meaning of Model(s, a). Does it mean that Model(s, a) is the history of rewards gained when we are in state s and the action a is taken? 

Does R, S_new = Model(s,a) mean that we should take a random sample from rewards gained in state s and action a is taken?
I think pseudocode was made for tabular case with an assumption of deterministic environment. $Model(s, a)$ would then be a table with information of the next state and reward after taking action $a$ from state $s$. The size of that table would be same as the size of Q table. Because the environment is deterministic you wouldn't take a random sample because there is only one possible transition so you would take the transition remembered in model table.