34 results about "Law of total probability" patented technology

The invention relates to a rapid analysis method for landing safety probability of a lunar probe. The rapid analysis method comprises the following steps: simulating landing of a lander on slopes of different angles to obtain slope threshold capable of turning over the lander; combining lunar surface landform models and detected data of Chang'e 2 to generate lunar surface landform models; dividing the lunar surface landform models into K landform models according to different slopes; performing N landing simulation experiments on each lunar surface landform model; analyzing according to the lunar surface data obtained by the Chang'e 2 to obtain a proportion Ci occupied by the slope ki in the lunar surface landform model; calculating a preliminary landing safety probability analysis result according to a full-probability formula. By using the technical scheme provided by the invention, a selection basis can be provided for a specific location for landing the lander on the lunar surface.

The invention discloses a distribution line risk probability assessment method based on historical factor analysis, comprising the following steps: (1), determining key factors for evaluating the risk probability of distribution lines; (2), aiming at statistical key factors, respectively Establish a probability calculation model for the emergence of key factors and a probability calculation model for distribution line tripping caused by key factors; (3), establish a calculation model for evaluating the risk probability of distribution lines for key factors that cannot be counted: (4) According to the steps ( 2) and the calculation model of step (3), calculate the distribution line risk probability corresponding to each key factor, and then calculate the overall risk probability of the distribution line in the evaluated area according to the following full probability formula. The method of the invention has high accuracy, provides decision-making basis for risk assessment of power distribution lines, can evaluate the risk probability of power distribution lines in a certain area, and can also evaluate the risk probability of a certain power distribution line according to actual conditions.

The present invention relates to a modeling method of cellular gene translation process, comprising the following steps: according to tracking the state of each mRNA molecule and updating the count in real time, obtaining the quantity that can be initialized in various mRNAs; according to tracking the state of each ribosome molecule and updating the count in real time Update the count to get the number of ribosomes that can move on various mRNA sites; calculate the total initialization speed of the mRNA molecule; calculate the total extension speed of the ribosome molecule on the corresponding mRNA; get the mRNA initialization according to the total initialization speed and the total extension speed and the total probability of ribosome extension; according to the weight of the event probability, an event is randomly selected to react; after the reaction, the cell state is updated; the present invention adopts the principle based on a completely asymmetric simple repulsion process, and considers the half-life of the molecule at the same time. Correct translation takes into account the possibility of wrong translation, making the translation process closer to the essence of biological problems.

The invention provides a Copula function-based multivariate hydrologic uncertainty processing method. The method can be used for hydrologic forecasting, and is characterized by comprising the following steps: step 1, collecting hydrometeorological basic data and quantitative precipitation forecast data of a basin; step 2, establishing a hydrologic model to obtain forecast discharge processes of different forecast periods; step 3, determining marginal distribution functions of actually measured flow and forecast flow; step 4, utilizing a Copula function to construct a joint probability distribution function of the actually measured flow and the forecast flow; step 5, solving a Bayesian posterior transition probability density function of the actually measured flow of the different forecast periods according to the marginal distribution functions estimated in the step 3 and the joint probability distribution function constructed in the step 4; and step 6, acquiring a Bayesian posterior joint probability density function of the actually measured discharge processes through a total probability formula according to the Bayesian posterior transition probability density function, obtained in the step 5, of the actually measured flow of the different forecast periods.

The invention provides a method for solving the state steady-state probability of a multi-state system under general distribution. The method can establish a system state equation for an arbitrary distributed system, and provides a method for obtaining the system steady-state probability. The transition of the system state is described as a generalized Markov process by using the supplementary variable method, and the system state equation is transformed into an ordinary differential equation due to the fact that the system state does not change according to absolute time in the steady state. Combined with the boundary conditions and initial conditions of the system, the system of equations is initially solved, and the important parameters in the state equation of the system are extracted by using the generalized integral mean value theorem, and the state equation of the system is transformed with the help of the equivalence relationship between the parameters. The generalized probability equation is obtained by using the total probability formula and solved by the method of least squares. This method can significantly reduce the difficulty of obtaining the probabilistic steady-state solution of each state of a multi-state system and has certain universality. It is suitable for solving problems such as system state analysis and maintenance strategy optimization in fields such as reliability engineering and mechanical engineering.