0000053987 00000 n 0000061496 00000 n Lognormal and Beta distributions 0000008268 00000 n 0000017745 00000 n 21 0 obj<>stream stream 0000058137 00000 n 0000034779 00000 n %%EOF 0000028982 00000 n 0000054524 00000 n 0000027762 00000 n 0000016050 00000 n endstream endobj 20 0 obj<> endobj 22 0 obj<>>> endobj 23 0 obj<> endobj 24 0 obj<> endobj 25 0 obj<> endobj 26 0 obj<>stream 0000001953 00000 n uuid:39144447-b865-4bae-b871-a7ab5f198e9c for class demo Part I Frequentist Statistics 4. <<8a4d7b15924543459b80df0e8dff9d7f>]>> 11 0 obj Microsoft Word - Distributions3.doc Statistics 104 (Colin Rundel) Lecture 23 April 16, 2012 9 / 21 deGroot 7.2,7.3 Bayesian Inference Example - Defective Parts, Redux ۇŕ̘�r��,n��O����m�zJ�`U��a��s�;�C�ي�PwD����� ��s96�bX����ނ��jA����I�N`�v��I��M�FpÔ�a���1MyS@I^�(&��Vhg�I��¯���0&�����t}y� -}�� @����{�[��U���+�T$�2&V��t��d�n��S���Il��+gRk�CwhtUk���i�憚}Kt� IZ�)� �4n }�#um��fYw��HV)��]O�U��h�i��n�UW5��v�^�����c#P���\���5�K�4_)���Kn�~�ѹ�_�U�e�� 2tĶn�ۻs ��T$u`��pK����? 0000001811 00000 n startxref Example { beta simulation Simulate from a Beta(2:7;6:3) distribution. 0000008625 00000 n 3. 0000062598 00000 n A random variable having a Beta distribution is also called a Beta random variable. 0000063178 00000 n 0000061744 00000 n 0000000016 00000 n Note that if α = β = 1, then f(x) = 1 and the distribution is just the uniform distribution for (0,1). 19 0 obj<> endobj )��5����Cs��������5�a�1k���0��W�gz� P� #�C# Note that it isn’t necessary to nd f X(x) explicitly and we can ignore the normalizing constants of both the Likelihood and Prior. �@v���q�@���h`����,b���T�Z0��;�ֳO`�q�e]%p�Q%r�H������E�L�?�hq|dP! 33 0 obj <>/Metadata 30 0 R/Pages 29 0 R/OpenAction 58 0 R/Type/Catalog>> endobj 30 0 obj <>stream x��W XT���33'�l�P�3\r����)��PF��� (�3�20�s�3þ��P�t�p��jfjY�rk�7���ߠ�oXԬ��a���{�����m��\�0�Ϭ����3'��~~��I� �>F���hE�[ 2008-03-05T12:26:52-05:00 LECTURE 8. Xk … 0000001535 00000 n 0000069290 00000 n 19 46 2008-03-05T12:26:52-05:00 0000072188 00000 n %���� 0000018613 00000 n �١�2�5�������CЭ��!m�G��8O�[��F�P�v�^R��� Q��_`��":�)��-_��]��������Q]�G��zh�܉>�P����W8�J��ܭ��C. 0000071794 00000 n 2000-03-29T14:07:39Z 0000003386 00000 n %PDF-1.6 %���� {i:�\0��Ʌg���� 0000035982 00000 n <> The lecture notes may cover more material than the lectures. xref 0000001216 00000 n Chapter 1 Likelihood, su ciency and ancillarity ... distributions, gamma (including exponential) distributions, and many more. 0000002595 00000 n 0000044703 00000 n Acrobat PDFWriter 4.0 for Windows NT 0000019030 00000 n 0000068897 00000 n x�b```b``��������A��bl,KZ�64:00t�`�x�Nmh|#�xBe���7C]��;]�)bx]G��:�D��k��c��Gxtu��8 �fRRR6F`R application/pdf This is a distribution that is sometimes used as a rough model in the absence of data as an alternative to the triangular distribution as follows: given estimates for minimum a, most likely c, and maximum b, if mean μ is also estimated, 0000071260 00000 n – 1 – Lisa Yan CS109 Lecture Notes #21 May 22, 2020 The Beta Distribution Based on a chapter by Chris Piech In this chapter we are going to have a very meta discussion about how we represent probabilities. %PDF-1.5 34 which means that the function f(xj ; ) = ( + ) ( )( ) x 1(1 x) 1 for x 2 [0;1] is a probability density function.
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