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@@ -83,6 +83,13 @@ dim_m = length(m_vec)
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83
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83
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dim_a = length(a_vec)
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84
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84
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dim_b = length(b_vec)
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85
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85
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86
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+err_qua_m = 0
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87
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+err_qua_a = 0
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88
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+err_qua_b = 0
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89
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+err_lin_b = 0
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90
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+err_lin_m = 0
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91
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+err_con_b = 0
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92
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+
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86
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93
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#Marginalization of posterior_quad over a
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87
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94
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int_qua_a=array(0, c(dim_m, dim_b))
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88
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95
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for (i in (1:dim_m)){
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...
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@@ -128,3 +135,16 @@ for (i in (1:length(b_vec))){
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128
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135
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int_lin_m=array(0, dim=c(dim_b))
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129
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136
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I=int(b_vec, norm(posterior_con))
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130
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137
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int_con_b=I
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138
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+
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139
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+
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140
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+
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141
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+# The integration via fit is computationally expensive compared to the simple numerical integration
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142
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+
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143
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+
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144
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+strqua = paste("quadratic: ax^2 + mx +b with a=",toString(best_a_qua),"+-",toString(err_qua_a),",\n m=", toString(best_m_qua),"+-",toString(err_qua_m),", b=",toString(best_b_qua),"+-",toString(err_qua_b))
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145
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+strlin = paste("linear: mx +b with m=", toString(best_m_lin),"+-",toString(err_lin_m),",\n b=",toString(best_b_lin),"+-",toString(err_lin_b))
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146
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+strcon = paste("constant: b with b=",toString(best_b_con),"+-",toString(err_con_b))
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147
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+print(strqua)
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148
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+text(0,2.8,strqua)
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149
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+text(0,2.1,strlin)
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150
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+text(0,1.6,strcon)
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