How can a machine learn from experience? Probabilistic modelling provides a framework for understanding what learning is, and has therefore emerged as one of the principal theoretical and practical approaches for designing machines that learn from data acquired through experience. The probabilistic framework, which describes how to represent and manipulate… CONTINUE READING

Figure 2 | Probabilistic programming. A probabilistic program in Julia (left) defining a simple three-state hidden Markov model (HMM), inspired by an example in ref. 62. The HMM is a widely used probabilistic model for sequential and time-series data, which assumes the data were obtained by transitioning stochastically between a discrete number of hidden states98. The first four lines define the model parameters and the data. Here ‘trans’ is the 3 × 3 state-transition matrix, ‘initial’ is the initial state distribution, and ‘statesmean’ are the mean observations for each of the three states; actual observations are assumed to be noisy versions of this mean with Gaussian noise. The function hmm starts the definition of the HMM, drawing the