In this talk I will introduce modern Monte Carlo methods, including state-of-the-art sequential Monte Carlo (SMC) and trans-dimensional Markov chain Monte Carlo (MCMC). After laying out the foundation, I will show how these flexible techniques are ideally suited for carrying out computation in sophisticated probabilistic models of cognition. In particular, I will show how they can be used to learn models with time-varying properties, unknown number of variables, and (possibly unknown) complex relational and hierarchical structures. I will also show how these methods can be used to attack problems in stochastic decision making, such as active learning, experimental design, optimal control and sequential Markov decision processes. |