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I have recently been experimenting with using Axiom for maximum likelihood estimation,
borrowing code (with permission) from "Compact Numerical Methods" by Nash.
\begin{spad}
)abbrev package OPTIM OptimNash
++ Author: M. Clements
++ Date Created: 24 August 2008
++ Date Last Updated:
++ Basic Functions:
++ Related Constructors:
++ Also See:
++ AMS Classification:
++ Keywords:
++ References:
++ Description:
++ Numerical optimisation using methods from Nash
++ Permission granted by Nash to translate from his Pascal code to Axiom
axisResults ==> Record(X: Vector Float, Fmin:Float, Lowerfun: Boolean)
nmminResults ==> Record(X:Vector Float, Fmin:Float, Fail:Boolean)
OptimNash: Exports == Implementation where 
    Exports == with 
        Rosen: Vector Float -> Float
                ++ Rosen(Bvec) returns the Rosen function
        nmmin: (Vector Float, (Vector Float -> Float), Float) -> nmminResults
                ++ nmmin(Bvec, fminfn, intol) returns values for the optimised function
        axissrch :(Vector Float, Vector Float -> Float) -> axisResults
                ++ axissrch(Bvec, fminfn) returns the axis search method
        demo: () -> nmminResults
                ++ demo() returns the rsults of a simple demo using the Rosen function
        demo2: () -> nmminResults
                ++ demo2() returns the results of a more complex demo
        --optimFun: (Expression Float,List Equation Expression Float) -> nmminResults
    Implementation == add 
        --writeln(Classical starting point (-1.2,1))
        Rosen(Bvec: Vector Float):Float == 
                (Bvec(2)-Bvec(1)^2)^2*100.0+(1.0-Bvec(1))^2
        --nmmin(vector([11.0, 1.0]), Rosen$OPTIM, -1.0)$OPTIM
        nmmin(Bvec: Vector Float,
                fminfn: Vector Float -> Float,
                intol: Float):nmminResults == 
                Pcol ==> 27
                Prow ==> 26
                alpha ==> 1.0
                beta ==> 0.5
                gamma ==> 2.0
                Calceps ==> 1.0e-35
                big ==> 1.0e35 -- as per R/src/main/optim.c
                n : Integer := #Bvec
                action    : String
                C         : Integer
                calcvert  : Boolean
                convtol   : Float
                f         : Float
                funcount  : Integer
                H         : Integer
                i         : Integer
                j         : Integer
                L         : Integer
                notcomp   : Boolean
                n1        : Integer
                oldsize   : Float
                P         : Matrix Float := new(Prow,Pcol,0.0)--[1..Prow,1..Pcol]
                shrinkfail: Boolean
                size      : Float
                step      : Float
                temp      : Float
                trystep   : Float
                VH,VL,VN  : Float
                VR        : Float
                x         : Vector Float := Bvec -- ugly declaration
                --writeln(Nash Algorithm 19 version 2 1988-03-17)
                --writeln(  Nelder Mead polytope direct search function minimiser)
                --writeln(confile,Nash Algorithm 19 version 2 1988-03-17)
                --writeln(confile,  Nelder Mead polytope direct search function minimiser)
                fail := false
                (f,notcomp) := (fminfn(Bvec),false)
                if notcomp then
                        --writeln(**** Function cannot be evaluated at initial parameters ****)
                        --writeln(confile,
                        --        **** Function cannot be evaluated at initial parameters ****)
                        fail := true
                else
                        --writeln(Function value for initial parameters = ,f)
                        --writeln(confile,Function value for initial parameters = ,f)
                        if intol<0.0 then intol := Calceps
                        funcount := 1
                        convtol := intol*(abs(f)+intol)
                        --writeln(  Scaled convergence tolerance is ,convtol)
                        --writeln(confile,  Scaled convergence tolerance is ,convtol)
                        n1 := n+1  
                        C := n+2  
                        P(n1,1) := f
                        for i in 1..n repeat P(i,1) := Bvec(i)
                        L := 1
                        size := 0.0
                        step := 0.0
                        for i in 1..n repeat if 0.1abs(Bvec(i))>step then step := 0.1abs(Bvec(i))
                        --writeln(Stepsize computed as ,step)
                        --writeln(confile,Stepsize computed as ,step)
                        for j in 2..n1 repeat
                                action := "BUILD          "
                                for i in 1..n repeat P(i,j) := Bvec(i)
                                trystep := step
                                while P(j-1,j)=Bvec(j-1) repeat
                                        P(j-1,j) := Bvec(j-1)+trystep 
                                        trystep := trystep*10.0
                                size := size+trystep
                        oldsize := size
                        calcvert := true
                        shrinkfail := false
                        repeat 
                                if calcvert then
                                        for j in 1..n1 repeat
                                                if j~=L then
                                                        for i in 1..n repeat Bvec(i) := P(i,j)
                                                        (f,notcomp) := (fminfn(Bvec),false)
                                                        if notcomp or f>=big then f := big 
                                                        funcount := funcount+1  
                                                        P(n1,j) := f
                                        calcvert := false
                                VL := P(n1,L)
                                VH := VL
                                H := L
                                for j in 1..n1 repeat
                                        if j~=L then
                                                f := P(n1,j)
                                                if fVH then
                                                        H := j 
                                                        VH := f
                                if VH>VL+convtol then
                                        --str(funcount:5,tstr)
                                        --writeln(action,tstr,' ,VH, ,VL)
                                        --writeln(confile,action,tstr, ,VH, ',VL)
                                        VN := betaVL+(1.0-beta)VH
                                        for i in 1..n repeat
                                                temp := -P(i,H)
                                                for j in 1..n1 repeat temp := temp+P(i,j)
                                                P(i,C) := temp/n
                                        for i in 1..n repeat
                                                Bvec(i) := (1.0+alpha)P(i,C)-alphaP(i,H)
                                        (f,notcomp) := (fminfn(Bvec),false)
                                        if notcomp or f>=big then f := big
                                        funcount := funcount+1
                                        action := "REFLECTION     "
                                        VR := f
                                        if VRBvec(i)+(1-gamma)P(i,C)
                                                        P(i,C) := Bvec(i)
                                                        Bvec(i) := f
                                                (f,notcomp) := (fminfn(Bvec),false)
                                                if notcomp or f>=big then f := big 
                                                funcount := funcount+1
                                                if fP(i,H)+betaP(i,C)
                                                (f,notcomp) := (fminfn(Bvec),false)
                                                if notcomp or f>=big then f := big 
                                                funcount := funcount+1
                                                if f=VH then
                                                        action := "SHRINK         "
                                                        calcvert := true
                                                        size := 0.0
                                                        for j in 1..n1 repeat
                                                                if j~=L then
                                                                    for i in 1..n repeat
                                                                        P(i,j) := beta*(P(i,j)-P(i,L))+P(i,L)
                                                                        size := size+abs(P(i,j)-P(i,L))
                                                        if sizePolytope size measure not decreased in shrink)
                                                                --writeln(confile,
                                                                --        Polytope size measure not decreased in shrink)
                                                                shrinkfail := true
                                if ((VH<=VL+convtol) or shrinkfail ) then break -- repeat loop
                --writeln(Exiting from Alg19.pas Nelder Mead polytope minimiser)
                --writeln('    ,funcount, function evaluations used')
                --writeln(confile,Exiting from Alg19.pas Nelder Mead polytope minimiser)
                --writeln(confile,'    ,funcount, function evaluations used')
                fmin := P(n1,L)
                for i in 1..n repeat x(i) := P(i,L)
                if shrinkfail then fail := true
                return [x, fmin, fail]

        axissrch(Bvec: Vector Float, fminfn: Vector Float -> Float): axisResults ==
            cradius, eps, f, fplus, step, temp, tilt, fmin: Float
            i : Integer
            --notcomp : Boolean
        --writeln(alg20.pas -- axial search)
            --writeln(confile,alg20.pas -- axial search)
            Calceps   : Float := 1.0e-35
            big       : Float := 1.0e35 -- as per R/src/main/optim.c
            n :Integer := #(Bvec)::NonNegativeInteger::Integer
            fmin := fminfn(Bvec)
            eps := Calceps
            eps := sqrt(eps)
            --writeln('  Axis':6,' Stepsize  ':14,'function +  ':14,
            --    'function -  :14, rad. of curv.:14,   tilt')
            --writeln(confile,'  Axis':6,' Stepsize  ':14,'function +  ':14,
            --    'function -  :14, rad. of curv.:14,   tilt')
            lowerfn := false
            for i in 1..n repeat
                    if (not lowerfn) then
                            temp := Bvec(i)
                            step := eps*(abs(temp)+eps)
                            Bvec(i) := temp+step
                            f := fminfn(Bvec)
                            if (f>=big) then f := big
                            --write(i:5,' ,step:12,  ,f:12,  )
                            --write(confile,i:5, ,step:12,  ,f:12,  )
                    if f<fmin then lowerfn := true
                    if (not lowerfn) then
                            fplus := f
                            Bvec(i) := temp-step
                            f := fminfn(Bvec)
                            if f>=big then f := big
                            --write(f:12,  )  write(confile,f:12,  ')
                    if f                            temp := 0.5(fplus-f)/step
                            fplus := 0.5(fplus+f-2.0fmin)/(stepstep)
                            if fplus~=0.0 then
                                    cradius := 1.0+temptemp
                                    cradius := cradiussqrt(cradius)/fplus
                            else
                                cradius := big
                            tilt := 45.0*atan(temp)/atan(1.0)
                            --write(cradius:12,'  ,tilt:12)
                            --write(confile,cradius:12,  ',tilt:12)
                    --writeln writeln(confile)
            [Bvec, f, lowerfn]

        demo(): nmminResults ==
            nmmin([-1.2, 1.0], Rosen, -1.0)
        demo2(): nmminResults ==
            --banner:=dr1920.pas -- driver for Nelder-Mead minimisation
            --startup
            --fminset(n,B,Workdata) {sets up problem and defines starting
            --               values of B}
            lowerfn:Boolean := false --{safety setting}
            B:Vector Float := [-1.2,1.0] --initial values
            mynmminResults :nmminResults
            myaxisResults : axisResults
            --n :Integer := #(B)::NonNegativeInteger::Integer
            repeat
                    mytol:=-1.0 --Note: set the tolerance negative to indicate that
                    --procedure must obtain an appropriate value.
                    mynmminResults := nmmin(B,Rosen,mytol) --{minimise the function}
                    --writeln
                    --writeln(confile)
                    --writeln( Minimum function value found =,Fmin)
                    --writeln( At parameters)
                    --writeln(confile, Minimum function value found =,Fmin)
                    --writeln(confile, At parameters)
                    --for i in 1..n repeat 
                    --{
                    --writeln(' B[',i,]=,X[i])
                    --writeln(confile,' B[',i,]=,X[i])
                    --} --{loop to write out parameters}
                    B := mynmminResults.X
                    myaxisResults := axissrch(B, Rosen)  --{alg20.pas}
                    lowerfn := myaxisResults.Lowerfun
                    if lowerfn then
                            B := myaxisResults.X
                            --writeln(Lower function value found)
                            --writeln(confile,Lower function value found)
                    if (not lowerfn) then break
            return mynmminResults
            --flush(confile) close(confile)
            --if infname<>con then close(infile)
            --{dr1920.pas -- Nelder Mead minimisation with axial search}
--      optimFun(e:Expression Float,initial:List Equation Expression Float):nmminResults == 
--              vars := [lhs(x) for x in initial]
--              vals := [rhs(x) for x in initial]::Vector Float
--              nmmin(vals, (x +-> eval(e, vars, x)), -1)
\end{spad}

\begin{spad}
)abbrev package SOMESTAT SomeStatisticsPackage
SomeStatisticsPackage: Exports == Implementation where
  DF  ==> DoubleFloat
  Exports ==> with
        pgamma:(Float,Float) -> Float
                ++ pgamma(y,p) returns the incomplete gamma function (Alg AS 147)
        erf:(Float) -> Float
                ++ erf(x) returns the error function
        erfc:(Float) -> Float
                ++ erfc(x) returns the complementary error function
        pnorm:(Float) -> Float
                ++ pnorm(x) returns the CDF for the normal(0,1) function
        pnorm:(Float,Float,Float) -> Float
                ++ pnorm(x,mean,sd) returns the CDF for a normal(mean,sd) function
        pchisq:(Float,Float) -> Float
        ppois:(Integer,Float) -> Float
        ppois2:(Integer,Float) -> Float
        rpois:(Float) -> Integer
                ++ rpois(lambda) returns a random Poisson variable with mean lambda
        rbinom:(Integer,Float) -> Integer
                ++ rbinom(n,p) returns a random binomial variable from n trials
                ++ and probability p
        mean:(Vector Float) -> Float
        mean:(List Float) -> Float
        mean:(Vector DF) -> DF
        mean:(List DF) -> DF
        var:(Vector Float) -> Float
        Beta:(Float,Float) -> Float
        betai:(Float,Float,Float) -> Float
        betacf:(Float,Float,Float) -> Float
        pbeta:(Float,Float,Float) -> Float
                ++ pbeta(x,a,b) returns the incomplete beta function betai(a,b,x)
        pt:(Float,Float) -> Float
                ++ pt(x,df) returns the CDF for a t distribution
        pbinom:(Float,Float,Float) -> Float
                ++ pbinom(k,n,p) returns the CDF for 0..k events given n trials with probability p 
        pf:(Float,Float,Float) -> Float
                ++ pf(f,df1,df2) returns the CDF of the F distribution with df1 and df1 degrees of freedom
  Implementation ==> add
        import FloatSpecialFunctions
        import RandomFloatDistributions
        pgamma(y,p) ==
                eps : Float := (10.0^(-digits()$Float+1))$Float
                g : Float := 0.0
                if (y=0.0 or p=0.0) then return g
                if (y < 0.0 or p < 0.0) then error "Invalid arguments"
                a : Float := p + 1.0
                f : Float := exp(p  log(y) - logGamma(a) - y)
                if (f < eps) then
                        return g
                c : Float := 1.0
                g : Float := 1.0
                a : Float := p
                while (c > eps  g) repeat 
                        a := a+1.0
                        c := c(y / a)
                        g := g+c
                g := gf
                return g
        erf(x:Float):Float == if x<0.0 then -erf(-x) else pgamma(x^2,0.5)
        erfc(x:Float):Float == 1-erf(x)
        pnorm(x:Float):Float == 0.5(1+erf(x/sqrt(2.0)))
        pnorm(x:Float,mu:Float,sigma:Float):Float == 0.5(1+erf((x-mu)/sigma/sqrt(2.0)))
        pchisq(x:Float,df:Float):Float == pgamma(x/2,df/2)
        ppois(y:Integer,lambda:Float):Float ==
                if y<0 then return 0.0
                reduce(_+,[exp(-lambda)lambda^yi/factorial(yi) for yi in 0..y])$List(Float)
        ppois2(y:Integer,lambda:Float):Float == 1-pgamma(lambda,y::Float+1)
        rpois(lambda:Float):Integer ==
                cumP : Float := 0
                x : Float := uniform01()
                for i in 0.. repeat
                        cumP := cumP + exp(-lambda)lambda^i/factorial(i)
                        if x1.0 then 
                        error "bad argument x in betai"
                bt := if x=0.0 or x=1.0 then 0.0 else 
                        x^a(1.0-x)^b/Beta(a,b)
--                      exp(logGamma(a+b)$FloatSpecialFunctions-
--                          logGamma(a)$FloatSpecialFunctions-
--                          logGamma(b)$FloatSpecialFunctions+
--                          alog(x)+blog(1.0-x))
                if x<(a+1.0)/(a+b+2.0) then return bt  betacf(a,b,x)/a else
                        return 1.0-btbetacf(b,a,1.0-x)/b
        betacf(a:Float,b:Float,x:Float):Float == 
                itmax : Integer := 1000
                eps : Float := (10.0^(-digits()$Float+1))$Float
                qab : Float := a+b
                qap : Float := a+1.0
                qam : Float := a-1.0
                c : Float := 1.0
                d : Float := 1.0-qabx/qap
                d := 1.0/d
                h : Float := d
                for m in 1..itmax repeat
                        em : Float := m::Float
                        m2 := 2.0em
                        aa : Float := m(b-em)x/((qam+m2)(a+m2))
                        d := 1.0+aad
                        c := 1.0+aa/c
                        d := 1.0/d
                        h := hdc
                        aa := -(a+em)(qab+em)x/((a+m2)(qap+m2))
                        d := 1.0+aad
                        c := 1.0+aa/c
                        d := 1.0/d
                        del : Float := dc
                        h := hdel
                        if abs(del-1.0)f))
\end{spad}

Now, let's try this.
\begin{axiom}
nmminResults ==> Record(X:Vector Float, Fmin:Float, Fail:Boolean)
optimFun(e:Expression Float,initial:List Equation Expression Float):nmminResults ==
        vars := [lhs(x) for x in initial]
        vals := [rhs(x) for x in initial]::Vector Float
        nmmin(vals, ((x:Vector Float):Float +-> eval(e, vars, x)), -1)$OPTIM
-- some examples
dpois := exp(-lambda)*lambda^y/Gamma(y+1)
negll1 := -log(eval(dpois, y=10.0))
optimFun(negll1, [lambda=5.0])
optimFun((b-a^2)^2*100.0+(1.0-a)^2, [a=11.0,b=1.0])
x := [xi::Float for xi in 1..5]
Y := [10, 15, 20, 25, 31]
negll1 := reduce(+, [eval(-log(dpois),[y=Y(i),lambda=exp(alpha+beta*x(i))]) for i in 1..#Y]);
out1 := optimFun(negll1, [alpha=2, beta=0])
-- R: summary(glm(c(10, 15, 20, 25, 31) ~ I(1:5), family=poisson))
mleResult ==> Record(coef:Matrix Float,vars:List Symbol, AIC:Float)
mle(negll:Expression Float, initial:List Equation Expression Float):mleResult ==
        vars := [lhs(x) for x in initial]::List Symbol
        vals := [rhs(x) for x in initial]::Vector Float
        out : nmminResults := nmmin(vals, (x +-> eval(negll, vars, x)), -1)$OPTIM
        estimate := out.X
        covMatrix := inverse(matrix([[eval(D(D(negll,x),y),vars,estimate) for y in vars] for x in vars]))
        se := [sqrt(covMatrix(i,i)) for i in 1..nrows(covMatrix)]
        zscore := [estimate(i)/se(i) for i in 1..nrows(covMatrix)]
        pvalues := [2(1-pnorm(zscore(i)::Float)$SOMESTAT) for i in 1..nrows(covMatrix)]
        AIC := out.Fmin2+2*#(out.X)
        coef := transpose(matrix([estimate,se,zscore,pvalues]))
        return [coef,vars,AIC]
mle(negll1, [alpha=2.0, beta=0.0])
negll2 := -log(eval(dpois, y=10.0))
mle(negll2, [lambda=5.0])
\end{axiom}
As an aside: can we calculate an elasticity from logistic regression?
\begin{axiom}
Y:=exp(alpha+betakPk)/(1+exp(alpha+betakPk))
D(Y,Pk)/Y
D(Y,Pk)/Y/(1-Y)
\end{axiom}


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Loading kernel: messages; fonts; allocations: state, direction, utility macros; pictures: \xy, positions, objects, decorations; kernel objects: directionals, circles, text; options; algorithms: directions, edges, connections; Xy-pic loaded)) (/usr/share/texmf-texlive/tex/generic/xypic/xyall.tex Xy-pic option: All features v.3.3 (/usr/share/texmf-texlive/tex/generic/xypic/xycurve.tex Xy-pic option: Curve and Spline extension v.3.7 curve, circles, loaded) (/usr/share/texmf-texlive/tex/generic/xypic/xyframe.tex Xy-pic option: Frame and Bracket extension v.3.7 loaded) (/usr/share/texmf-texlive/tex/generic/xypic/xycmtip.tex Xy-pic option: Computer Modern tip extension v.3.3 (/usr/share/texmf-texlive/tex/generic/xypic/xytips.tex Xy-pic option: More Tips extension v.3.3 loaded) loaded) (/usr/share/texmf-texlive/tex/generic/xypic/xyline.tex Xy-pic option: Line styles extension v.3.6 loaded) (/usr/share/texmf-texlive/tex/generic/xypic/xyrotate.tex Xy-pic option: Rotate and Scale extension v.3.3 loaded) (/usr/share/texmf-texlive/tex/generic/xypic/xycolor.tex Xy-pic option: Colour extension v.3.3 loaded) (/usr/share/texmf-texlive/tex/generic/xypic/xymatrix.tex Xy-pic option: Matrix feature v.3.4 loaded) (/usr/share/texmf-texlive/tex/generic/xypic/xyarrow.tex Xy-pic option: Arrow and Path feature v.3.5 path, \ar, loaded) (/usr/share/texmf-texlive/tex/generic/xypic/xygraph.tex Xy-pic option: Graph feature v.3.7 loaded) loaded) (/usr/share/texmf-texlive/tex/latex/tools/verbatim.sty) (/usr/share/texmf/tex/latex/graphviz/graphviz.sty (/usr/share/texmf-texlive/tex/latex/psfrag/psfrag.sty)) (/usr/share/texmf/tex/latex/sagetex.sty Writing sage input file 3789055917543086017-16.0px.sage ) (/usr/share/texmf-texlive/tex/latex/gnuplottex/gnuplottex.sty (/usr/share/texmf-texlive/tex/latex/base/latexsym.sty) (/usr/share/texmf-texlive/tex/latex/moreverb/moreverb.sty) (/usr/share/texmf-texlive/tex/latex/base/ifthen.sty)) (./3789055917543086017-16.0px.aux) (/usr/share/texmf-texlive/tex/latex/ucs/ucsencs.def) Missing $ inserted. <inserted text> $ l.153 (Bvec(2)-Bvec(1)^ 2)^2*100.0+(1.0-Bvec(1))^2 (/usr/share/texmf-texlive/tex/latex/jknapltx/ursfs.fd) (/usr/share/texmf-texlive/tex/latex/amsfonts/umsa.fd) (/usr/share/texmf-texlive/tex/latex/amsfonts/umsb.fd) (/usr/share/texmf-texlive/tex/latex/base/ulasy.fd) Missing $ inserted. <inserted text> $ l.154
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Overfull \hbox (27.39969pt too wide) in paragraph at lines 155--223 []\T1/cmr/m/n/12 --nmmin(vector([11.0, 1.0]), Rosen$\OML/cmm/m/it/12 OPTIM; \OM S/cmsy/m/n/12 ^^@\OT1/cmr/m/n/12 1\OML/cmm/m/it/12 :\OT1/cmr/m/n/12 0)$\T1/cmr/ m/n/12 OPTIM nm-min(Bvec: Vec-tor Float, fminfn: Vec-tor Float -> Float, in-tol : Float):nmminResults [1] You can't use `macro parameter character #' in horizontal mode. l.333 n :Integer := # (Bvec)::NonNegativeInteger::Integer You can't use `macro parameter character #' in horizontal mode. l.388 --n :Integer := # (B)::NonNegativeInteger::Integer [2] Missing $ inserted. <inserted text> $ l.464 eps : Float := (10.0^ (-digits()$Float+1))$Float Missing $ inserted. <inserted text> $ l.494 cumP := cumP + exp(-lambda)*lambda^ i/factorial(i) Missing $ inserted. <inserted text> $ l.504 mean(x:Vector Float):Float == reduce(_ +,x)/#x You can't use `macro parameter character #' in math mode. l.504 ...n(x:Vector Float):Float == reduce(_+,x)/# x You can't use `macro parameter character #' in math mode. l.505 mean(x:List Float):Float == reduce(_+,x)/# x You can't use `macro parameter character #' in math mode. l.506 mean(x:Vector DF):DF == reduce(_+,x)/# x You can't use `macro parameter character #' in math mode. l.507 mean(x:List DF):DF == reduce(_+,x)/# x You can't use `macro parameter character #' in math mode. l.510 reduce(_+,[(x(i)-meanx)^2 for i in 1..# x])$List(Float)/(#x-1) You can't use `macro parameter character #' in horizontal mode. l.510 ...-meanx)^2 for i in 1..#x])$List(Float)/(# x-1) Missing $ inserted. <inserted text> $ l.527 eps : Float := (10.0^ (-digits()$Float+1))$Float Missing $ inserted. <inserted text> $ l.557 \end{spad} \newpage

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Overfull \hbox (65.58339pt too wide) in paragraph at lines 422--557 \OML/cmm/m/it/12 meanx \OT1/cmr/m/n/12 : \OML/cmm/m/it/12 Float \OT1/cmr/m/n/12 := \OML/cmm/m/it/12 mean\OT1/cmr/m/n/12 (\OML/cmm/m/it/12 x\OT1/cmr/m/n/12 )\O ML/cmm/m/it/12 reduce\OT1/cmr/m/n/12 ([]\OML/cmm/m/it/12 ; \OT1/cmr/m/n/12 [(\O ML/cmm/m/it/12 x\OT1/cmr/m/n/12 (\OML/cmm/m/it/12 i\OT1/cmr/m/n/12 ) \OMS/cmsy/ m/n/12 ^^@ \OML/cmm/m/it/12 meanx\OT1/cmr/m/n/12 )[]\OML/cmm/m/it/12 foriin\OT1 /cmr/m/n/12 1\OML/cmm/m/it/12 ::x\OT1/cmr/m/n/12 ])$\T1/cmr/m/n/12 List(Float)/ (x-1) --Gamma(x:Float):Float == Gamma(x)$\OML/cmm/m/it/12 FloatSpecialFunctions Beta\OT1/cmr/m/n/12 (\OML/cmm/m/it/12 a \OT1/cmr/m/n/12 : [3] (/usr/share/texmf-texlive/tex/latex/base/t1cmtt.fd)

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[4] [5] (./3789055917543086017-16.0px.aux) ) (see the transcript file for additional information) Output written on 3789055917543086017-16.0px.dvi (5 pages, 21476 bytes). Transcript written on 3789055917543086017-16.0px.log.

Subject: Be Bold !!

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