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4. Value individual transactions (facilities) depending on the likelihood of default assigned to the obligor in #3. 5. Calculate portfolio loss by summing results for all transactions. 6. Repeat steps 1 6 some number of times to map the loss distribution. In factor models, correlation in default rates is driven by the coef cients on the various factors. That is, the state of the economy causes all default rates and transition probabilities to change together. A low state of economic activity drawn from the simulation of macrovariables produces high default/downgrade probabilities, which affect all obligors in the portfolio, thereby producing correlation in default/migration risk. Ignoring risk that is unique to each rm (i.e., risk that is not explained by the factors), any two rms that have the same factor sensitivities will have perfectly correlated default rates. [See Gordy (2000).] The rst widely discussed macrofactor model was introduced by McKinsey & Company and was called CreditPortfolioView. In order to be able to compare a Macro Factor Model with the other credit portfolio models, Rutter Associates produced a Demonstration Model that is similar to the McKinsey model.

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and handle a large feature space. The unresolved problem of the supervised approach is the presence of multiple classes and class imbalance; a function can be associated with only a few genes, and there are several thousand functions describing genes in a given microarray data set. 4.3.5 Correlations Among Gene Expression Pro les

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Treasure, J, Todd, G, Brolly, M, Tiller, J, Nehmed, A & Denman, F (1995) A pilot study of a randomised trial of cognitive analytical therapy vs educational behavioral therapy for adult anorexia nervosa Behaviour Research and Therapy, 3, 363 367 Vitousek, KB & Hollon, SD (1990) The investigation of schematic content and processing in eating disorders Cognitive Therapy and Research, 14, 191 214 Vitousek, KB, & Ewald, LS (1993) Self-representation in eating disorders: A cognitive perspective In ZV Segal & SJ Blatt (Eds), The Self in Emotional Distress: Cognitive and Psychodynamic Perspectives New York: Guilford Press Vitousek, KB Daly, J & Heiser, C (1991) Reconstructing the internal world of the eating-disordered individual: Overcoming denial and distortion in self-report International Journal of Eating Disorders, 10, 647 666 Vitousek, KM.

with We have two choices now: we could fuse {o1 , o2 , o3 } {o4 , o5 }, or {o4 , o5 } with {o6 } since in either case the intercluster dissimilarity is 5. We choose the rst option and we form the cluster {o1 , o2 , o3 , o4 , o5 }. Now the partition is 5 = {{o1 , o2 , o3 , o4 , o5 }, {o6 }, {o7 }}

(1996) The current status of cognitive-behavioral models of anorexia nervosa and bulimia nervosa In PM Salkovskis (Ed), Frontiers of Cognitive Therapy New York: Guilford Press Vitousek, K & Manke, F (1994) Personality variables and disorders in anorexia nervosa and bulimia nervosa Journal of Abnormal Psychology, 103, 103 147 Vitousek, K, Watson, S & Wilson, GT (1998) Enhancing motivation for change in treatmentresistant eating disorders Clinical Psychology Review, 18, 391 420 Wadden, TA, Sternberg, JA, Letizia, KA, Stunkard, AJ & Foster, GD (1989) Treatment of obesity by very low calorie diet, behavior therapy, and their combination: A ve-year perspective Journal of Consulting and Clinical Psychology, 66, 429 433 Wadden, TA, Vogt, RA, Foster, GD & Anderson, DA.

congestion in the network, and the signal used to detected congestion divides TCP CC algorithms into three main classes: Loss based. These algorithms use packet loss as a signal that the network is congested. The protocols probe the available capacity by increasing w until loss occurs due to overflowing buffers, at which point they decrease w before probing again. These protocols tend to fill up the buffers with their probing action and operate around the BDPD + B region on the graph. Delay based. These algorithms detect increases in RTT to determine that the buffers are filling up when w is greater than the BPDP. These protocols operate around the BPDP region on the graph and tend to operate with low queuing delay. Explicit signal. These protocols rely on additional information being communicated by routers and switches to determine the right value for w. The router typically computes a congestion level based on the input traffic rate and capacity, and signals the congestion level to the sources using additional fields in the packet header, such as the Explicit Congestion Notification (ECN) field [2]. Note that any given source is not explicitly aware of any other sources using the same network path. However, each source s use of the network path contributes to the packet loss, or delay experienced by other sources. By measuring the packet loss, delay, or explicit signal, the sources can coordinate among themselves in a distributed way to determine what their fair rate is. Before describing the detailed mechanisms of how different TCP variants adjust the w from these signals, it is important to understand what this fairness goal really means.

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