A computer-implemented method for determining a remaining useful life, RUL, or probability of failure, PoF, of a power system asset or industrial asset (11-13; 16-18), the method comprising: determining transition probabilities of a discrete Markov Chain model from historical data comprising sensor data for a plurality of assets, wherein the discrete Markov Chain model has a state space that comprises a set of asset health states, the method characterized in that plural sets of transition probabilities are determined, each associated with different operating conditions and/or ambient conditions; and in that the method further comprises performing, in parallel, a plurality of independent stochastic simulations using the transition probabilities of the discrete Markov Chain model, wherein each of the plurality of independent stochastic simulations simulates a future evolution in the state space of the discrete Markov Chain model over a prognostic horizon, and wherein the plurality of independent stochastic simulations are performed in parallel such that one or several first simulations are performed using a first set of transition probabilities associated with first operating conditions and/or first ambient conditions, and one or several second simulations are performed using a second set of transition probabilities associated with second operating conditions and/or second ambient conditions, wherein the first transition probabilities are different from the second transition probabilities; computing a prognostic asset health state evolution (60; 80) over the prognostic horizon from the plurality of independent stochastic simulations, wherein computing the prognostic asset health state evolution (60; 80) comprises: computing a RUL of the power system asset or industrial asset (11-13; 16-18), or computing a PoF of the power system asset or industrial asset (11-13; 16-18); and generating output based on the computed prognostic asset health state evolution. the method characterized in that plural sets of transition probabilities are determined, each associated with different operating conditions and/or ambient conditions; and in that the method further comprises performing, in parallel, a plurality of independent stochastic simulations using the transition probabilities of the discrete Markov Chain model, wherein each of the plurality of independent stochastic simulations simulates a future evolution in the state space of the discrete Markov Chain model over a prognostic horizon, and wherein the plurality of independent stochastic simulations are performed in parallel such that one or several first simulations are performed using a first set of transition probabilities associated with first operating conditions and/or first ambient conditions, and one or several second simulations are performed using a second set of transition probabilities associated with second operating conditions and/or second ambient conditions, wherein the first transition probabilities are different from the second transition probabilities; computing a prognostic asset health state evolution (60; 80) over the prognostic horizon from the plurality of independent stochastic simulations, wherein computing the prognostic asset health state evolution (60; 80) comprises: computing a RUL of the power system asset or industrial asset (11-13; 16-18), or computing a PoF of the power system asset or industrial asset (11-13; 16-18); and generating output based on the computed prognostic asset health state evolution. performing, in parallel, a plurality of independent stochastic simulations using the transition probabilities of the discrete Markov Chain model, wherein each of the plurality of independent stochastic simulations simulates a future evolution in the state space of the discrete Markov Chain model over a prognostic horizon, and wherein the plurality of independent stochastic simulations are performed in parallel such that one or several first simulations are performed using a first set of transition probabilities associated with first operating conditions and/or first ambient conditions, and one or several second simulations are performed using a second set of transition probabilities associated with second operating conditions and/or second ambient conditions, wherein the first transition probabilities are different from the second transition probabilities; wherein each of the plurality of independent stochastic simulations simulates a future evolution in the state space of the discrete Markov Chain model over a prognostic horizon, and wherein the plurality of independent stochastic simulations are performed in parallel such that one or several first simulations are performed using a first set of transition probabilities associated with first operating conditions and/or first ambient conditions, and one or several second simulations are performed using a second set of transition probabilities associated with second operating conditions and/or second ambient conditions, wherein the first transition probabilities are different from the second transition probabilities; computing a prognostic asset health state evolution (60; 80) over the prognostic horizon from the plurality of independent stochastic simulations, wherein computing the prognostic asset health state evolution (60; 80) comprises: computing a RUL of the power system asset or industrial asset (11-13; 16-18), or computing a PoF of the power system asset or industrial asset (11-13; 16-18); and generating output based on the computed prognostic asset health state evolution.