1. A method of configuring a device for detecting a situation from among a set of situations (S- 1 ,. .. , S-N) where a physical system is observed by a least one sensor, comprising the following steps: receiving a sequence of observation data of the physical system, called a training sequence (L- 1 ,. .. , L-N), provided by the sensor and corresponding to a determined situation of the physical system, determining, from the training sequence, the parameters of a statistical hidden Markov model (HMM- 1 ,. .. , HMM-N) recorded onto the detection device's storage media and relating to the determined situation, by prior initializing these parameters, then updating these initialized parameters, wherein the prior initialization comprises the following steps: with the statistical model being used having a given number of hidden states, determining multiple probability distributions from the training sequence, by dividing the sequence into sub-sequences and assigning to each sub-sequence a probability distribution statistically modeling the respective sub-sequence, the number of determined probability distributions being greater than the number of hidden states in the statistical model being used, distributing said determined probability distributions among the hidden states of the statistical model being used such that a plurality of probability distributions are assigned to at least one of the hidden states, determining, for each hidden state in the statistical model being used and, from the probability distributions assigned to said hidden state, a single probability distribution that is representative of said hidden state, and initializing the parameters of the statistical model being used from the determined representative probability distributions, and wherein the method also includes a configuration step for the detection device such that the statistical model being used includes the parameters determined by said prior initialization and then said update, wherein, each probability distribution being a normal distribution, the single probability distribution that is representative of a hidden state Ki is a normal distribution that represents the center thereof and that is determined by the calculation of its expectation μ i and its variance Σ i based on the expectations μ i,j and variances Σ i,j of all probability distributions of this hidden state Ki, as follows: μ i = 1 Card ⁡ ( Ki ) ⁢ ∑ j ∈ Ki ⁢ μ i , j ⁢ ⁢ and ⁢ ⁢ Σ i = 1 Card ⁡ ( Ki ) ⁢ ∑ j ∈ Ki ⁢ ( Σ i , j + μ i , j H ⁢ μ i , j ) - μ i H ⁢ μ i , where Card is the “Cardinal” function and H is the Hermitian transpose.