Real World Exposure and CVA Simulation

Although this pricing is done independently in all forward model steps, risk neutral pricing and market conditions are evolved from the same spot market data through probabilistic processes, but this is done in a specific subset of probability paths It is the same as adjusting risk-neutral pricing. Please take general countermeasures. Below is an overview of this simple and well-known implementation method, but we will further enhance it to incorporate the details of the actual simulation and the obvious advantages of risk neutral Monte Carlo pricing.

Real-world data is definitely worth more than analog data. In fact, real-world data is a bottleneck, so companies can not run many useful simulation scenarios. Tesla has created a technical miracle called a "massive distributed mobile data center" that collects real world driving data that can only be learned through simulation. The reason for collecting a large amount of data is to support a deep neural network of cars. This is short background. Deep Neural Network became popular in 2012 after deep neural network acquired ImageNet challenge. ImageNet challenge is a computer vision competition focused on image classification. In 2015, Deep Neural Network slightly exceeded the benchmark of ImageNet Challenge for the first time. (Interestingly, the human benchmark is artificial intelligence researcher Andrej Karpathy, now Tesla 's AI director.)

3 / analog world. It is difficult to test the real world. Google is driving five million miles on autonomous vehicles, ideally you want billions of dollars. Can you verify that these virtual tests are properly adjusted to the real world after building a simulation test environment that can capture real world clips and reassemble them into Monte Carlo simulations? The network data method here also allows for more possibilities. In London it can not be a good general example

Simulating the brain on a computer may be an interesting study, but like the actual brain, it is best understood by their response to the real world. To test the simulated brain in a real environment, some researchers, such as Edelman, use robotic-like devices, others use computer avatars, others use computer vision We will try to achieve object recognition focusing on. Edelman emphasizes that the real world interaction formed the evolution of the brain. He developed the theory he calls neurological Darwinism, focusing on the reward as the driving force of brain evolution. "The brain is embodied and the body and brain are embedded in the real world environment," Edelman said. "And its very affluent environment provides rewards that drive real brains to make choices."