An inattentive state determination device, comprising: a camera for imaging a driver; observed amount acquiring means for acquiring the head yaw angle as information on the driver's line of sight based on an image of the driver, and at least one of either information on driving operations and information on a traveling state as an observed amount; inattentive state determining means for determining a current inattentive state of the driver a) based on the stochastic relationship between a known inattentive state and a known observed amount, as well as a current observed amount regarding at least one of either information on driving operations and information on a traveling state acquired by using the observed amount acquiring means by calculating a probability that the driver is driving inattentively; and b) based on the current observed amount of head yaw angle acquired by using the observed amount acquiring means, wherein the inattentive state determining means determines that the driver is driving inattentively when the calculated probability exceeds a threshold (ξ) and the head yaw angle exceeds a threshold (φ); and further comprising: inattentive state learning means for learning a transition model and an observation model, wherein the inattentive state learning means learns the transition model and the observation model in advance, wherein the inattentive state determining means calculates the probability that the driver is driving inattentively based on the transition model and the observation model, which are learned by the inattentive state learning means, and on the current observed amount, wherein the transition model is a conditional probability distribution of each state X when two or more states change from one to another, and it is represented as P (X t+1 =ON | X t =OFF), and wherein the observation model is a conditional probability distribution of the observed amounts and it is represented as P (Z t (k) =z | X t =ON), wherein Z (k) shows the value of the k-th observed amount of N observed amounts. a camera for imaging a driver; observed amount acquiring means for acquiring the head yaw angle as information on the driver's line of sight based on an image of the driver, and at least one of either information on driving operations and information on a traveling state as an observed amount; inattentive state determining means for determining a current inattentive state of the driver a) based on the stochastic relationship between a known inattentive state and a known observed amount, as well as a current observed amount regarding at least one of either information on driving operations and information on a traveling state acquired by using the observed amount acquiring means by calculating a probability that the driver is driving inattentively; and b) based on the current observed amount of head yaw angle acquired by using the observed amount acquiring means, a) based on the stochastic relationship between a known inattentive state and a known observed amount, as well as a current observed amount regarding at least one of either information on driving operations and information on a traveling state acquired by using the observed amount acquiring means by calculating a probability that the driver is driving inattentively; and b) based on the current observed amount of head yaw angle acquired by using the observed amount acquiring means, wherein the inattentive state determining means determines that the driver is driving inattentively when the calculated probability exceeds a threshold (ξ) and the head yaw angle exceeds a threshold (φ); and further comprising: inattentive state learning means for learning a transition model and an observation model, wherein the inattentive state learning means learns the transition model and the observation model in advance, wherein the inattentive state determining means calculates the probability that the driver is driving inattentively based on the transition model and the observation model, which are learned by the inattentive state learning means, and on the current observed amount, wherein the transition model is a conditional probability distribution of each state X when two or more states change from one to another, and it is represented as P (X t+1 =ON | X t =OFF), and wherein the observation model is a conditional probability distribution of the observed amounts and it is represented as P (Z t (k) =z | X t =ON), wherein Z (k) shows the value of the k-th observed amount of N observed amounts. inattentive state learning means for learning a transition model and an observation model, wherein the inattentive state learning means learns the transition model and the observation model in advance, wherein the inattentive state determining means calculates the probability that the driver is driving inattentively based on the transition model and the observation model, which are learned by the inattentive state learning means, and on the current observed amount, wherein the transition model is a conditional probability distribution of each state X when two or more states change from one to another, and it is represented as P (X t+1 =ON | X t =OFF), and wherein the observation model is a conditional probability distribution of the observed amounts and it is represented as P (Z t (k) =z | X t =ON), wherein Z (k) shows the value of the k-th observed amount of N observed amounts.