Method for estimating a probability distribution of a maximum coefficient of friction (μ) at a current and a future waypoint (s, s*) of a vehicle, in which a first probability distribution for the maximum coefficient of friction (μ) at the current waypoint (s) of the vehicle is determined from a first number of items of information which are or have been determined for the current waypoint (s) of the vehicle and characterize the maximum coefficient of friction (μ) at the current waypoint (s) of the vehicle, wherein the first probability distribution is determined by means of a Bayesian network, wherein a second probability distribution for the maximum coefficient of friction (μ) at the future waypoint (s*) of the vehicle is estimated from a second number of items of information relating to the future waypoint (s*), in particular by means of the Bayesian network; wherein a resulting probability distribution (WV res) is determined from the first and second probability distributions (WV1, WV2) by means of a combination, wherein, in order to determine the first probability distribution from the first number of items of information, an a priori probability distribution (WV pr) is determined for the maximum coefficient of friction (μ); wherein, for each output node (y(s)) of the Bayesian network, a likelihood probability distribution (WV li,i) is determined from the first number of items of information on the basis of the observation of a specific output value at the current waypoint (s); wherein, for at least some of the output nodes (y(s)), a respective a posteriori probability distribution (WV po,i) is determined for the maximum coefficient of friction (μ) from the a priori probability distribution (WV pr) and the respective likelihood probability distribution (WV li,i) in a correction step with the aid of Bayes' formula, wherein the respective a posteriori probability distribution (WV po,i) for the maximum coefficient of friction (μ) is assessed, wherein a vehicle acceleration is additionally taken into account when determining the likelihood probability distribution (WV li,i), characterized in that the respective a posteriori probability distribution (WV po,i) is assessed, and one of the a posteriori probability distributions (WV po,i) is selected, on the basis of an entropy. a first probability distribution for the maximum coefficient of friction (μ) at the current waypoint (s) of the vehicle is determined from a first number of items of information which are or have been determined for the current waypoint (s) of the vehicle and characterize the maximum coefficient of friction (μ) at the current waypoint (s) of the vehicle, wherein the first probability distribution is determined by means of a Bayesian network, wherein a second probability distribution for the maximum coefficient of friction (μ) at the future waypoint (s*) of the vehicle is estimated from a second number of items of information relating to the future waypoint (s*), in particular by means of the Bayesian network; wherein a resulting probability distribution (WV res) is determined from the first and second probability distributions (WV1, WV2) by means of a combination, wherein, in order to determine the first probability distribution from the first number of items of information, an a priori probability distribution (WV pr) is determined for the maximum coefficient of friction (μ); wherein, for each output node (y(s)) of the Bayesian network, a likelihood probability distribution (WV li,i) is determined from the first number of items of information on the basis of the observation of a specific output value at the current waypoint (s); wherein, for at least some of the output nodes (y(s)), a respective a posteriori probability distribution (WV po,i) is determined for the maximum coefficient of friction (μ) from the a priori probability distribution (WV pr) and the respective likelihood probability distribution (WV li,i) in a correction step with the aid of Bayes' formula, wherein the respective a posteriori probability distribution (WV po,i) for the maximum coefficient of friction (μ) is assessed, wherein a vehicle acceleration is additionally taken into account when determining the likelihood probability distribution (WV li,i), characterized in that the respective a posteriori probability distribution (WV po,i) is assessed, and one of the a posteriori probability distributions (WV po,i) is selected, on the basis of an entropy.