fig1 is a block diagram showing schematically a probabilistic model generation apparatus according to a first embodiment of the present invention . the probabilistic model generation apparatus includes a financial database db 1 , a bankruptcy information database db 2 , a financial data reader a 1 , a financial data storage a 2 , a bankruptcy information reader a 3 , a bankruptcy information storage a 4 , a financial index value upper and lower limit setter a 5 ( hereinafter simply , referred to as upper and lower limit setter ), a post - conversion index value storage a 6 , a bankruptcy probability calculator a 7 , and a bankruptcy probability storage a 8 . the upper and lower limit setter a 5 , for example , corresponds to a conversion function generator . the bankruptcy probability calculator a 7 , for example , corresponds to a model generator . processing conducted in each of units shown in fig1 may be conducted in one computer device or may be conducted in a plurality of computer devices distributively . furthermore , the processing conducted in each of units may be implemented by causing a cpu to execute a previously created program , using hardware , or using a combination of them . the financial data reader a 1 reads financial index values of enterprises in certain fiscal year from the financial database db 1 . fig2 shows an example of financial data stored in the financial database db 1 . five financial indexes ( attributes ) 1 to 5 ( gross profit ratio on sales , owned capital ratio , ordinary income , accounts receivable collection period , and sales increase ratio ) are shown in fig2 . each of values of the financial indexes 1 to 5 in one enterprise corresponds to an explanatory variable . specified financial index values ( such as , for example , owned capital ratio , sales increase ratio , and ordinary income increase rate ) of a specified enterprise group ( for example , enterprises of the building industry ) are read out from data having a form shown in fig2 , and stored in the financial data storage a 2 . the financial data storage a 2 may be a memory such as a dram ( dynamic random access memory ). if the data amount is large , the financial data storage a 2 may be a hard disk . the bankruptcy information reader a 3 reads bankruptcy information of enterprises from the bankruptcy information database db 2 , and stores it in the bankruptcy information storage a 4 . the bankruptcy information contains information as to whether bankruptcy has occurred a year or less after a predetermined reference day ( such as account day of the enterprise ). the bankruptcy information storage a 4 may be a memory such as a dram . if the data amount is large , the bankruptcy information storage a 4 may be a hard disk . fig3 shows an example of bankruptcy information of enterprises stored in the bankruptcy information database db 2 . “ 1 ” in the bankruptcy information represents that the enterprise has gone bankrupt a year or less after , whereas “ 0 ” in the bankruptcy information represents that the enterprise has not gone bankrupt a year or less after . bankruptcy information corresponds to , for example , a target variable , and “ bankruptcy ” corresponds to , for example , a predetermined event . a set of explanatory variables and a target variable corresponds to , for example , a sample . in the present embodiment , the bankruptcy information database db 2 and the financial database db 1 are provided separately . data in these databases db 1 and db 2 may be stored in one database , and the present invention incorporates this case . the upper and lower limit setter a 5 receives financial index values from the financial data storage a 2 and bankruptcy information from the bankruptcy information storage a 4 , and converts financial index values as described hereafter . a value obtained by converting an ith financial index value x ij of a jth enterprise is denoted by y ij . y corresponds to , for example , an intermediate variable . when converting a financial index value by using the logit conversion , the conversion is represented by ( expression 1 ), where α and β are parameters in the conversion . y ij = 1 1 + exp ⁡ ( α i ⁢ x ij + β i ) ( expression ⁢ ⁢ 1 ) since the logistic function gradually approaches 0 or 1 , this conversion is equivalent to setting upper and lower limit values of the financial index value . an example of a graph of the logistic function is shown in fig4 . as for the conversion parameters α and β , learning is conducted to maximize the logarithmic likelihood : 1 ⁢ ( α i , β i ) = log ⁢ ∏ j = 1 n ⁢ ⁢ y ij c j ⁡ ( 1 - y ij ) ( 1 - c j ) = ∑ j = 1 n ⁢ ( c j ⁢ log ⁢ ⁢ y ij + ( 1 - c j ) ⁢ log ⁡ ( 1 - y ij ) ) ( expression ⁢ ⁢ 2 ) in the case of the logistic function , optimization of the logarithmic likelihood is the so - called convex problem . therefore , the optimization of the logarithmic likelihood can be conducted by using a well - known technique such as the newton method or the steepest descent method . in ( expression 2 ), n represents the number of enterprises stored in the financial data storage a 2 , and c j is a random variable . if the jth enterprise will go bankrupt within one year , the random variable c j assumes 1 . if the jth enterprise will not go bankrupt within one year , the random variable c j assumes 0 . the upper and lower limit setter a 5 stores the financial index value y after conversion in the post - conversion index value storage a 6 . the post - conversion index value storage a 6 may be a memory such as a dram . if the data amount is large , the post - conversion index value storage a 6 may be a hard disk . an example of conversion of financial index values 1 ( gross profit ratios on sales ) in fig2 conducted by using the ( expression 1 ) is shown in fig5 . financial index values of enterprises before the conversion are converted into a range of 0 to 1 . the bankruptcy probability calculator a 7 receives the financial index value y after conversion from the post - conversion index value storage a 6 , and calculates a bankruptcy probability of an enterprise by using a logit model ( a binary logit model based on linear combination of financial indexes after the conversion ) represented by ( expression 3 ). p j = 1 1 + exp ⁡ ( ∑ i = 1 m ⁢ γ i ⁢ y ij + δ ) ( expression ⁢ ⁢ 3 ) here , p j is a bankruptcy probability of the jth enterprise , and m is a total number of financial indexes used to calculate the bankruptcy probability . as for conversion parameters γ and ε , learning is conducted to maximize the logarithmic likelihood 1 ⁢ ( γ , δ ) = log ⁢ ∏ j = 1 n ⁢ p j c j ⁡ ( 1 - c j ) = ∑ j = 1 n ⁢ ( c j ⁢ log ⁢ ⁢ p j + ( 1 - c j ) ⁢ log ⁡ ( 1 - p j ) ) ( expression ⁢ ⁢ 4 ) by using the newton method or the like . thus , in the present embodiment , after the parameters of the financial index value conversion are learned , parameters of a logit model are learned . the bankruptcy probability calculator a 7 stores the calculated bankruptcy probability p in the bankruptcy probability storage a 8 . the bankruptcy probability storage a 8 may be a memory such as a dram . if the data amount is large , the bankruptcy probability storage a 8 may be a hard disk . fig6 shows bankruptcy probabilities of the enterprises calculated using the ( expression 1 ) and ( expression 3 ) on the basis of the data shown in fig2 and fig3 . it is thus possible to calculate a probability that an enterprise to be evaluated will go bankrupt within a year on the basis of financial index values of the enterprise by using the ( expression 1 ) with the parameters α and β determined and the ( expression 3 ) with the parameters γ and δ determined . in more detail , financial index values of a certain enterprise are input from an input unit which is not illustrated . input financial indexes may be the same as those used when determining the parameters α and β . the upper and lower limit setter a 5 receives the financial index values input from the input unit into x in the ( expression 1 ), converts the financial index values , and then stores the financial index values after conversion in the post - conversion index value storage a 6 . the bankruptcy probability calculator a 7 reads out the financial index values after conversion from the post - conversion index value storage a 6 , inputs the financial index values read out into y in the ( expression 3 ), and calculates a probability that the enterprise will go bankrupt within one year . the bankruptcy probability calculator a 7 stores the calculated bankruptcy probability in the bankruptcy probability storage a 8 . heretofore , the ( expression 1 ) has been used as the logit conversion expression of the financial index values . alternatively , a conversion expression obtained by adding a quadratic term and a logarithmic term to the ( expression 1 ) may be used . as an example , ( expression 5 ) is obtained by adding a quadratic term to the ( expression 1 ). y ij = 1 1 + exp ⁡ ( α i ⁢ x ij + α i ⁢ x ij 2 + β i ) ( expression ⁢ ⁢ 5 ) a financial index which is not linear in relation between its financial index value and bankruptcy probability can also be modeled by thus adding the quadratic term and the logarithmic term . conventionally , the upper and lower limit value of a financial index values are determined by an expert having expert knowledge concerning financial statements and actual operation as shown in fig1 . in this example , financial index values of at least 100 are converted to 100 and financial index values of − 100 or less are converted to − 100 . “ 100 ” and “− 100 ” in these original financial index values correspond to the upper limit and the lower limit , respectively . conventionally , expert knowledge is needed to set the upper and lower limit values , and the upper and lower limit values cannot be determined simply . on the other hand , in the present embodiment , parameters of the logistic function for converting a financial index value are learned , and parameters of a probabilistic model for calculating the bankruptcy probability of an enterprise from the financial index value after conversion are learned . it can be said that learning of parameters of the logistic function is substantially equivalent to setting the upper and lower limit values . in the present embodiment , the upper and lower limit values can be thus set automatically and a probabilistic model having high precision can be generated simply . in the first embodiment , parameters of the financial index value conversion and the parameters of the logit model are learned separately . according to a feature of the second embodiment , however , parameters of the financial index value conversion and the parameters of the logit model are learned at a time . hereafter , the present embodiment will be described in detail . fig7 is a block diagram showing schematically a probabilistic model generation apparatus according to the second embodiment of the present invention . the probabilistic model generation apparatus includes a financial database db 11 , a bankruptcy information database db 12 , a financial data reader b 1 , a financial data storage b 2 , a bankruptcy information reader b 3 , a bankruptcy information storage b 4 , a financial index value upper and lower limit setter & amp ; bankruptcy probability calculator b 5 ( hereinafter simply referred to as upper and lower limit setter & amp ; bankruptcy probability calculator ), a post - conversion index value storage b 6 , and a bankruptcy probability storage b 7 . the financial data reader b 1 reads financial data of enterprises in certain fiscal year from the financial database db 11 . the financial data reader b 1 reads specified financial index values ( such as , for example , owned capital ratio , sales increase ratio , and ordinary income increase rate ) of a specified enterprise group ( for example , enterprises of the building industry ) from data having a form shown in fig2 , and stores the specified financial index values in the financial data storage b 2 . the financial data storage b 2 may be a memory such as a dram . if the data amount is large , the financial data storage b 2 may be a hard disk . the bankruptcy information reader b 3 reads bankruptcy information of enterprises ( whether the enterprises went bankrupt one year or less after ) as shown in fig3 from the bankruptcy information database db 12 , and stores it in the bankruptcy information storage b 4 . the bankruptcy information storage b 4 may be a memory such as a dram . if the data amount is large , the bankruptcy information storage b 4 may be a hard disk . the upper and lower limit setter & amp ; bankruptcy probability calculator b 5 receives financial index values from the financial data storage b 2 and bankruptcy information from the bankruptcy information storage b 4 , converts financial index values , and calculates the bankruptcy probability as described in detail hereafter . a value obtained by converting an ith financial index value x ij of a jth enterprise is denoted by y ij . when converting a financial index value by using the logit conversion , the conversion is represented by ( expression 6 ), where α and β are parameters in the conversion . y ij = 1 1 + exp ⁡ ( α i ⁢ x ij + β i ) ( expression ⁢ ⁢ 6 ) since the logistic function gradually approaches 0 or 1 , this conversion is equivalent to setting upper and lower limit values of the financial index value . a bankruptcy probability of an enterprise is calculated by using a logit model represented by ( expression 7 ). p j is a bankruptcy probability of the jth enterprise , and m is a total number of financial indexes used to calculate the bankruptcy probability . p j = 1 1 + exp ⁡ ( ∑ i = 1 m ⁢ γ i ⁢ y ij + δ ) ( expression ⁢ ⁢ 7 ) as for conversion parameters α , β , γ and δ in the ( expression 6 ) and ( expression 7 ), learning is conducted to maximize the logarithmic likelihood 1 ⁢ ( α , β , γ , δ ) = log ⁢ ∏ j = 1 n ⁢ p j ( 1 - c j ) = ∑ j = 1 n ⁢ ( c j ⁢ log ⁢ ⁢ p j + ( 1 - c j ) ⁢ log ⁡ ( 1 - p j ) ) ( expression ⁢ ⁢ 8 ) by using the newton method or the like . or the parameters may be learned so as to maximize the logarithmic likelihood by utilizing a network having a structure shown in fig8 ( which can be said to be a special neural network in that an intermediate node is not connected to all input nodes ). in ( expression 8 ), n represents the number of enterprises stored in the financial data storage b 2 and c j is a random variable . if the jth enterprise goes bankrupt within one year , the random variable c j assumes 1 . if the jth enterprise does not go bankrupt within one year , the random variable c j assumes 0 . the financial index value y after conversion is stored in the post - conversion index value storage b 6 . the post - conversion index value storage b 6 may be a memory such as a dram . if the data amount is large , the post - conversion index value storage b 6 may be a hard disk . on the other hand , the calculated bankruptcy probability p is stored in the bankruptcy probability storage b 7 . the bankruptcy probability storage b 7 may be a memory such as a dram . if the data amount is large , the bankruptcy probability storage b 7 may be a hard disk . as heretofore described , it is possible to calculate a probability that an enterprise to be evaluated will go bankrupt within a year on the basis of financial index values of the enterprise by using the ( expression 6 ) with the parameters α and β determined and the ( expression 7 ) with the parameters γ and δ determined . in more detail , financial index values of a certain enterprise are input from an input unit which is not illustrated . input financial indexes may be the same as those used when determining the parameters α , β , γ and δ . the upper and lower limit setter & amp ; bankruptcy probability calculator b 5 receives the financial index values input from the input unit into x in the ( expression 6 ), converts the financial index values , and then stores the financial index values after conversion in the post - conversion index value storage b 6 . the upper and lower limit setter & amp ; bankruptcy probability calculator b 5 reads out the financial index values after conversion from the post - conversion index value storage b 6 , inputs the financial index values read out into y in the ( expression 7 ), and calculates a probability that the enterprise will go bankrupt within one year . the upper and lower limit setter & amp ; bankruptcy probability calculator b 5 stores the calculated bankruptcy probability in the bankruptcy probability storage b 7 . according to the present embodiment , the parameters of the financial index value conversion and the parameters of the logit model are learned at a time , as heretofore described . as a result , the total time required for parameter calculation can be shortened . in the present embodiment , a broken line function is adopted as the function for converting the financial index values . hereafter , the present embodiment will be described with reference to fig1 . the financial data reader a 1 reads specified financial index values ( such as , for example , owned capital ratio , sales increase ratio , and ordinary income increase rate ) of a specified enterprise group ( for example , enterprises of the building industry ) from the financial database db 1 which stores financial data of enterprises in certain fiscal year , and stores the read financial index values in the financial data storage a 2 . the bankruptcy information reader a 3 reads bankruptcy information of enterprises ( whether the enterprises went bankrupt one year or less after ) from the bankruptcy information database db 2 , and stores it in the bankruptcy information storage a 4 . the upper and lower limit setter a 5 receives financial index values from the financial data storage a 2 and bankruptcy information from the bankruptcy information storage a 4 , and converts the financial index values . here , a value obtained by converting an ith financial index value x ij of a jth enterprise is denoted by y ij . in the present embodiment , the financial index values are converted by using a broken line function . for example , the following method is used . it is now supposed that { b 1 , . . . , b k + 1 } are section boundary points of the financial index x i ( where b 1 and b k + 1 are minimum and maximum values of the financial index x i , respectively ), and { q 1 , . . . , q k } are bankruptcy probabilities corresponding to respective sections . in other words , q k =( the number of bankrupt enterprises corresponding to a section k )/( the number of all enterprises corresponding to the section k ) ( expression 9 ) furthermore , { m 1 , . . . , m k } are middle points in respective sections ( in other words , m t =( b t + b t + 1 )/ 2 , t = 1 , . . . , k ). a value y ij obtained by conversion of the financial index value x ij is defined by the following ( expression 10 ). { q 1 , b 1 ≦ x ij ≦ m 1 q t + 1 - q t m t + 1 - m t ⁢ ( x - m t ) + q t , m t ≦ x ij ≦ m t + 1 , 1 ≦ t ≦ k - 1 q k , m k ≦ x ij ≦ b k + 1 ( expression ⁢ ⁢ 10 ) this conversion corresponds to conversion using a broken line function . fig9 shows a graph example of a broken line function . this example corresponds to setting the upper and lower limit values respectively to 20 and 100 in the financial index before conversion . here , section boundary points { b 2 , . . . , b k } are found so as to maximize logarithmic likelihood l ( b 2 , . . . , b k ) defined by the following ( expression 11 ) by using the financial index values and the bankruptcy information as learning data . for example , values of respective elements are found so as to maximize the logarithmic likelihood with respect to each of { b 2 , b 3 }, { b 2 , b 3 , b 4 }, { b 2 , b 3 , b 4 , b 5 }, 1 ⁢ ( b 2 , … ⁢ , b k ) = log ⁢ ∏ j = 1 n ⁢ y ij ⁢ ( 1 - y ij ) ( 1 - c j ) cj = ∑ j = 1 n ⁢ ( c j ⁢ log ⁢ ⁢ y ij + ( 1 - c j ) ⁢ log ⁡ ( 1 - y ij ) ) ( expression ⁢ ⁢ 11 ) here , n represents the number of enterprises stored in the financial data storage a 2 , and c j is a random variable . if the jth enterprise goes bankrupt within one year , the random variable c j assumes 1 . if the jth enterprise does not go bankrupt within one year , the random variable c j assumes 0 . as for a method for determining the number k of sections , there is , for example , a method of utilizing the financial index value and the bankruptcy information in a different fiscal year as test data and adopting the number k of sections which maximizes the logarithmic likelihood 1 ′ ⁢ ( b 2 , … ⁢ , b k ) = log ⁢ ∏ j = 1 n ′ ⁢ y ij ′ c j ′ ⁡ ( 1 - y ij ′ ) ( 1 - c j ′ ) = ∑ j = 1 n ′ ⁢ ( c j ′ ⁢ log ⁢ ⁢ y ij ′ + ( 1 - c j ′ ) ⁢ log ⁡ ( 1 - y ij ′ ) ) ( expression ⁢ ⁢ 12 ) for the test data . for example , with respect to each of { b 2 , b 3 }, { b 2 , b 3 , b 4 }, { b 2 , b 3 , b 4 , b 5 }, . . . found using the ( expression 11 ), logarithmic likelihood of test data is calculated using the ( expression 12 ), and the number of sections which maximizes the logarithmic likelihood of test data is selected out of them . here , n ′ represents the number of enterprises in the different fiscal year utilized for the calculation , and c ′ j is a random variable . if the jth enterprise goes bankrupt within one year , the random variable c ′ j assumes 1 . if the jth enterprise does not go bankrupt within one year , the random variable c ′ j ; assumes 0 . the upper and lower limit setter a 5 stores the financial index value y after conversion in the post - conversion index value storage a 6 . the bankruptcy probability calculator a 7 receives the financial index value y after conversion from the post - conversion index value storage a 6 , and calculates a bankruptcy probability of an enterprise by using a logit model represented by ( expression 13 ). p j = 1 1 + exp ⁡ ( ∑ i = 1 m ⁢ γ i ⁢ y ij + δ ) ( expression ⁢ ⁢ 13 ) here , p j is a bankruptcy probability of the jth enterprise , and m is a total number of financial indexes used to calculate the bankruptcy probability . as for conversion parameters γ and δ , learning is conducted to maximize the following logarithmic likelihood . 1 ⁢ ( γ , δ ) = log ⁢ ∏ j = 1 n ⁢ p j c j ⁡ ( 1 - p j ) ( 1 - c j ) = ∑ j = 1 n ⁢ ( c j ⁢ log ⁢ ⁢ p j + ( 1 - c j ) ⁢ log ⁡ ( 1 - p j ) ) ( expression ⁢ ⁢ 14 ) the bankruptcy probability calculator a 7 stores the calculated bankruptcy probability p in the bankruptcy probability storage a 8 . according to the present embodiment , parameters of the broken line function for converting the financial index value are learned and parameters of a probabilistic model for calculating the bankruptcy probability of the enterprise from the financial index value after conversion are learned , as heretofore described . it can be said that the learning of the parameters of the broken line function is equivalent to setting the upper and lower limit values . in the present embodiment , the upper and lower limit values can be thus set automatically and a probabilistic model having high precision can be generated simply . in the present embodiment , it is attempted to evaluate the probability of occurrence of a loss accident in office work conducted at an operation branch of a bank . first , office work is divided into unit office work ( such as new passbook issue and remittance ). change of office work in banks to online has been promoted , and consequently it is possible to easily count times of occurrence of various kinds of business such as new passbook issue and remittance . if standard time is previously set for each business to represent necessary time , therefore , a business amount in each business can be calculated from a product of the standard time and the number of times . as appreciated from the calculation method , the business amount is represented by a time period . here , the business amount is handled on the second time scale . with respect to certain business , the business amount thus calculated is divided into unit businesses having equal lengths beforehand and the length of the unit business is made sufficiently short . in the present embodiment , the length of the unit business is set equal to 3 , 600 seconds . at this time , occurrence of loss accidents twice in one unit business is rare and consequently it is supposed that such a case is disregarded . therefore , the number of loss accident times in the unit business becomes 0 or 1 . supposing that the business amount of certain business in a bank per month is 100 hours , 100 unit businesses exist . in certain business , each of individual unit businesses is identified by j . if an accident has occurred within the unit business , then c j = 1 . if an accident has not occurred within the unit business , then c j = 0 . explanatory variables are supposed to be indexes shown in fig1 . the explanatory variables is represented as vector x j =( x 1j , . . . , x 16j ). with respect to certain business executed in the same operation branch , all indexes of vector x j in each unit business are supposed to be equal . furthermore , as for the vector x j , index values obtained half a year before are used . in other words , as shown in fig1 , a model is constructed to estimate the loss accident occurrence probability half a year hence on the basis of the vector z j ( here , for convenience , z j is used instead of x j ) and data of c j half a year after a year of the z j . for example , if the number of unit businesses in an operation branch a is 100 and two loss accidents have occurred , then data shown in fig1 are obtained with respect to the operation branch a ( all of za are the same vectors ). in the same way , data are generated with respect to other operation branches as well , and data concerning all operation branches are put together as shown in fig1 . by using the data , a model having high precision is generated simply in the same way as the first to third embodiments . as for a mistake in hospital care , an instance in which a mistake is found before execution of care or an instance in which the patient has not been affected despite a mistake is called incident . on the other hand , an instance in which the patient is affected by a mistake , an instance in which the patient becomes dead or disabled , or an instance in which heavy treatment or remedy becomes needed is called accident . in the present embodiment , a model for calculating an accident occurrence probability is constructed for each nursing person . whether a nursing person causes an accident during a predetermined half year is represented by a variable c . in other words , if a nursing person j causes an accident , c j = 1 . if a nursing person j does not cause an accident , c j = 0 . since the nursing person j scarcely causes accidents twice during a half year , c assumes 0 or 1 in the present embodiment as well . furthermore , indexes shown in fig1 are measured during the same half year . these values are represented as vector x j =( x 1j , . . . , x 16j ). the vector x j =( x 1j , . . . , x 16j ) corresponds to the explanatory variables . here , a model is generated for data of {( x j , c j )| j = 1 , . . . , n } in the same way as the first to third embodiments . n represents the number of nursing persons . in the present embodiment , the vector x j and c j are data measured during the same time period . for estimating the probability of c j = 1 , therefore , it is necessary to estimate a future vector x j and substitute this value into the model . the following use is also conceivable . if the probability of c j = 1 is high even though c j = 0 , it is judged that a risk is not actualized although the risk is present , then a countermeasure is taken .