Modular stochastic machine and related method

A probability calculation method to be carried out on at least two random variables, the method comprising the steps of:

• providing a modular stochastic machine (10 ) comprising:
• at least one stochastic distribution module (SD) corresponding to a plurality of random variables (O, D, Z), the stochastic distribution module (SD) being able to receive an input of values of specified random variables from among the plurality of random variables, and to return as output, a representation of the distribution of probabilities of at least one non-specified random variable conditioned by the values of specified random variables received as input, and
• at least two stochastic variable modules (SV), each stochastic variable module (SV) corresponding to a single random variable (O, D, Z) and comprising:
• a stochastic multiplier (SPO) able to receive, as input, representations of a first probability distribution (P1(X)) and a second probability distribution (P2(X)) of a same random variable (X), and to return, as output, a representation of the produced probability distribution (P(X)) of said random variable (X), and
• a stochastic proportional normalizer (SPN) able to receive, as input, a representation in bit form of a probability distribution, and to return, as output, a proportionally normalized representation of the received probability distribution, the proportionally normalized representation comprising more bits at 1 than the representation received as input,
• a representation of a probability distribution on a random variable being a temporal series of stochastic bit vectors called sample-vectors, each sample-vector comprising as many coordinates as the cardinal of the random variable, the number of bits at 1 in the temporal series for the coordinates of a same rank being proportional to the probability value corresponding to said rank,
• the stochastic bit vectors being sample-values, a sample-value comprising only one bit at 1,
• each stochastic variable module (SV) comprising a stochastic sampler (SPS) able to be activated and deactivated, a stochastic sampler (SPS) being able to assume, as input, sample-vectors representing a probability distribution, and to return, as output, sample-values representing the same probability distribution,
• each stochastic variable module (SV) being suitable for operating according to a regime, called deterministic regime, in which the stochastic variable module (SV) is able to deliver a representation of a fixed value of the random variable, and
• the machine (10) comprising as many stochastic variable modules (SV) as there are random variables on which the calculation is to be done;
• activating or deactivating a stochastic sampler (SPS) of at least one stochastic variable module (SV) based on the probability calculation to be done;
• configuring a stochastic variable module (SV) so that the stochastic variable module (SV) operates according to the deterministic regime, and
• connecting or disconnecting data buses between certain stochastic variable modules (SV) and certain stochastic distribution modules (SD) based on the probability calculation to be done.

A probability calculation method to be carried out on at least two random variables, the method comprising the steps of:

• providing a modular stochastic machine (10) comprising:
• at least one stochastic distribution module (SD) corresponding to a plurality of random variables (O, D, Z), the stochastic distribution module (SD) being able to receive an input of values of specified random variables from among the plurality of random variables, and to return as output, a representation of the distribution of probabilities of at least one non-specified random variable conditioned by the values of specified random variables received as input, and
• at least two stochastic variable modules (SV), each stochastic variable module (SV) corresponding to a single random variable (O, D, Z) and comprising:
• a stochastic multiplier (SPO) able to receive, as input, representations of a first probability distribution (P1(X)) and a second probability distribution (P2(X)) of a same random variable (X), and to return, as output, a representation of the produced probability distribution (P(X)) of said random variable (X), and
• a stochastic proportional normalizer (SPN) able to receive, as input, a representation in bit form of a probability distribution, and to return, as output, a proportionally normalized representation of the received probability distribution, the proportionally normalized representation comprising more bits at 1 than the representation received as input,
• a representation of a probability distribution on a random variable being a temporal series of stochastic bit vectors called sample-vectors, each sample-vector comprising as many coordinates as the cardinal of the random variable, the number of bits at 1 in the temporal series for the coordinates of a same rank being proportional to the probability value corresponding to said rank,
• the stochastic bit vectors being sample-values, a sample-value comprising only one bit at 1,
• each stochastic variable module (SV) comprising a stochastic sampler (SPS) able to be activated and deactivated, a stochastic sampler (SPS) being able to assume, as input, sample-vectors representing a probability distribution, and to return, as output, sample-values representing the same probability distribution,
• each stochastic variable module (SV) being suitable for operating according to a regime, called deterministic regime, in which the stochastic variable module (SV) is able to deliver a representation of a fixed value of the random variable, and
• the machine (10) comprising as many stochastic variable modules (SV) as there are random variables on which the calculation is to be done;
• activating or deactivating a stochastic sampler (SPS) of at least one stochastic variable module (SV) based on the probability calculation to be done;
• and
• connecting or disconnecting data buses between certain stochastic variable modules (SV) and certain stochastic distribution modules (SD) based on the probability calculation to be done.