Textbook Group meeting for Parr, Pezzulo, Friston 2022 .

Hello everyone, it's June 23rd, 2022, and we are in week 8 of the textbook group cohort 1. We're starting chapter 4, and we'll be continuing with chapter 4 next week. We're more than halfway done with the time and with the chapters that we're going through in the first half of the book. So let's go to chapter 4, and just raise your hand and gather or write in the chat if you have anything that you want to address. So I'm going first to the math overviews page, where I've written some overviews for the previous chapters of varying levels of completeness. But this is very important as we set off into chapter 4. So I'm page 64. This chapter is more technical than chapters 1 through 3, appealing to linear algebra, differentiation, and a Taylor series expansion. Those readers interested in the details may turn to the appendices, dot dot dot. Those who do not want to delve into the theoretical underpinnings may skip this chapter. So keep that in mind as we continue on. And it can be an area of discussion and learning and such. But let's approach these themes and formalisms with the authors forewarning that this is something we can look for more detail in the appendixes, as well as skip this chapter, even if we might disagree, of course. Okay. Any other overall comments on chapter 4? Just for whomever read it, even a part of it. What was their overall perspective on chapter 4? What was it trying to do? What approach did they take? What approach did they take? What do they take? Yeah, Ali, and then anyone else? I think if we take the materials in chapters 1 through 3 as the foundational materials on which the active inference theory is supposed to build, well this chapter 4 is one of the first steps toward building the actual theory and I mean going beyond just the basics and foundational materials. So using the tools and foundations established in previous chapters, we are now perhaps ready to tackle the problem of actually constructing the generative models in two different situations as discrete time models and as continuous one. Awesome, thanks. Yes, in the chapters page, we can recall back to chapter 1 that just laid out the structure of the book. Chapter 2 provided the low road to active inference, which began with Bayesian inference, and talked about a few other prerequisite or preliminary themes, including introducing variational and expected free energy as imperatives, in the sense that they're able to be bounding surprise. Chapter 3 introduced the high road to active inference, which was starting not from the mechanistic kind of nucleus of the Bayes equation, but rather from the imperative for survival and persistence. Also introducing, in a first pass, the Markov blanket concept in partitioning. Chapter 4 is indeed when we start to get into many details that were not covered in earlier sections. It's going to first begin by bringing us closer to this connection between Bayesian inference and the free energy evaluation. And then this central idea of a generative model is discussed. That is going to be described in discrete time, specifically using the POMDP formalism, partially observable Markov decision process, and then as well as in continuous time. Then there's going to be some very interesting figures and formalisms and discussions on what generative models underlie predictive coding and motor reflexes, which is moving us towards Chapter 5, which is going to have some empirical work, mainly cited out, and some discussion on the plausible neurobiologies that can be implementing or modeled as implementing or modeled with the kinds of generative models of which have been prepared for in Chapters 2 and 3, motivated in Chapters 2 and 3 really, and then described in their essence in Chapter 4. Also, just a reminder that in the math group activities, but we're all in the math group, we're all in this learning journey together, we've been striving to make the natural language descriptions for equations. So, that would be…