Next question is from Nick Zanace. What's a good way to program farmers carries into your workout? What are the perks of going heavier versus walking farther? We're getting questions like this because a lot of people are enrolling to do some strong training. Everybody's enrolling in MAP Strong because that's the program that's on sale. And so in that program, we programmed farmer walks because, well, Robert Oberst really liked them, said they're really important. And now that I've done them regularly, I 100% agree, regardless of what your fitness goals are, heavy farmer walks have tremendous benefits for the entire body. It's one of the best exercises. I literally will put in the top 10 now of exercises that I've done that have really worked the whole body, built strength, built muscle, and have improved my squats and my deadlifts and all that other stuff. But how do you program it? A lot of people have this question because it kind of works everything. Where do I put it? Now, if you follow a MAPs program, we program it in four, you don't have to worry about it. Or if you follow a full body workout, really doesn't matter because you're working your whole body anyway, in which case I would suggest doing farmer workouts after legs and maybe at the beginning of your back workout. But what if you do a body part split? Where would you put a farmer walk? Two places I can think of that would make sense, either back day or leg day. I think those are the two days that I think farmer walks would be programmed on a split. I don't know, what do you guys think? Yeah, I like that. I also like it as a way to start my workout, too. I mean, we talked about this the other day. This was one of the exercises that we, yeah.  But because it's one of the single exercises that I can think of that literally wakes up your entire body, from your neck all the way down to your toes. I love to start it with a workout. I'll do two or three nice heavy carries. And then when I go into any other exercise after that, I feel like I'm so primed. And it reinforces good posture. Right. So yeah, it stacks your spine, it gets all the joints awake and alive. And so I do like it as a primer. And like you said before that, I thought it might exhaust me, but I've actually used this a few times before going into my compound lifts and loved it. But I also like using farmer walks for building volume. So we do this whole like these work sessions within, you know, map strong. And that's really to build up this tank, this motor, this ability to carry heavy things for longer amounts of time. And I just find that so valuable when you're going back into heavy lifting in your workouts, it's like you have that endurance for all those lifts. So now I can get through those lifts more effectively because I just have this bigger tank. Oh dude, one of the biggest benefits I got from map strong was my recovery ability went through the roof because it builds your ability to handle more and more volume and workload. So then I would go back to another maps program and like my body was recovering so fast and it realized it was because strong help build that up. Now the last part, he's asking about the perks of going heavier versus further. Is there a recommendation that you guys have with it? Do you prefer to go heavier or go further? So yeah, it depends kind of like what I'm doing, but like if it's for building volume, I like to go a bit lighter and then for further distance. And so that's something that I actually prefer to do it that way. I know like in the strongman training, they do a lot of like sprints with it, with like heavier loads and they kind of run with it, which has value in itself for that kind of more explosive, you know, type of strength. But for me personally, I do the longer distance. So I've used it as a strength building exercise where I go heavy and I'll go 50 yards, which is relatively short distance to carry away. And I've gotten up to 450 something pounds for that distance, no wrist straps, just my hands.  Like it builds my body like a deadlift would. So we're all kind of a little bit different. So I like in here because we have, it's our place and I can take my shoes off. I love to get barefoot in them and actually walk like really controlled. So I'm doing it moderately heavy. I'm not going like the heaviest I can. I would do that. It was sneakers and kind of moving relatively fast to the 50 yards in back like what Sal's talking about. But because I'm using it more like a primer to wake everything up, I like to get my shoes off, get barefoot, pull and like really think about my posture and like every step I take. So I'll go for a shorter distance, but I'll move slower and really think about my entire body the way I'm holding my shoulders, where my neck is positioned each foot and as my foot's kind of striking the ground and kind of gripping the floor with my feet. To me, I love that. I think for most people that what you just said, Adam, is probably the most valuable way to do a former walk. I think if you're advanced, high level, you wanna build maximal strength, you've already got good support, you've got good stability, been working out for a while, then you can have fun with seeing, you know, how heavy you can go for distance. In which case then it becomes, like I said, a strength in building exercise, but most people would benefit from farmer carries from doing it with the intent of being perfect, perfect with their form and their technique.


We walked together in Karnataka for 557 km in the Bharat Cholayat. I met lakhs and lakhs of people and all of you complained about one thing. The BJP's loot of the people of Karnataka. Karnataka youth are worried. They're worried about not getting jobs. Women are being harassed by price rises. And so we asked ourselves what would you expect from the Congress party and you gave us five extremely powerful answers. Guarantee number one, Grehar Lakshmi ski. 2000 rupees a month to every woman head of a household. With Grehar Lakshmi, we're keeping the money in the hands of women to ensure that they're in control and in charge of their own life. Guarantee number two, Yuvanidhi. With Yuvanidhi, we will provide support to the youth of Karnataka for two years. Rupees 3000 per month for unemployed graduates and rupees 1500 per month for unemployed diploma holders. We know that you're suffering the largest unemployment crisis that you have faced. We focus on creating 2.5 lakh government jobs and 10 lakh private sector jobs. Guarantee number three, Anabhagya. 10 kgs of rice per person per month to BPL families. With Anabhagya, we will ensure that not a single person in Karnataka goes to sleep. Guarantee number four, Grehar Jyoti. 200 units of free electricity for every single household. We know that price rise has impacted everyone and Karnataka households are no different. Guarantee number five, the Sakhi program. Free bus tickets for women to travel around Karnataka. We want women to travel safely and comfortably. Travel should not be a burden on the women of Karnataka. These five ideas are your ideas. Everybody who believes that what the BJP did over the last three years, the 40% commission's Sarkara and the gross robbery that they carried out from the people of Karnataka was wrong. Vote for a government that is sensitive and has a vision. On the 13th of May, when the results come, the Congress party will have a sweeping majority. On the first day, on the first cabinet meeting, these five guarantees will be given to you by the Congress party. We will return what is rightfully yours and set Karnataka on the right track of prosperity. This is not all we are going to do. This is the beginning of the whole movement.


Hello everyone, this is Alice Gao. In the previous video, I gave you some high-level ideas about why we want to use the variable elimination algorithm. If we use this algorithm, we can compute a probability based on a Bayesian network more efficiently than if we were to use the joint distribution directly. In this video, let's talk about the details about the variable elimination algorithm. First of all, a bit of an introduction. Our goal with Bayesian networks is that we want to perform probabilistic inference. But unfortunately, performing probabilistic inference is challenging. It's a difficult problem. If we want to compute the posterior distribution of one or more query variables given some evidence, this problem is actually belongs to a complexity class called sharp NP. Even if we think about a simpler problem, if we want to estimate the posterior probability based on a Bayesian network within an absolute error, so we're not calculating it exactly, we're only estimating it given some error bound, this problem is already NP hard. So performing probabilistic inference is generally challenging and we cannot hope for a general efficient implementation to do this task. However, we have to do this task anyway. And there are two main approaches for probabilistic inference for Bayesian networks. Exact inference and approximate inference. The difference are exactly in the names. Either we calculate the probabilities exactly, or we do some approximation and allow us to make small mistakes, allow us to calculate the number within some error bound. In this course, I only have time to discuss exact inference, but you can refer to the textbook for algorithms on approximate inference. So in order to do exact inference to calculate the probabilities exactly, one naive approach is that we can take a look at the joint probability distribution or the Bayesian network, and then we can enumerate all of the worlds that are consistent with the evidence that we observe, right? All of the worlds where the values of the observed variables are same as what they should be. This is naive because it sounds like it's terribly inefficient. So we can do much better than this by making use of the variable elimination algorithm. We can do better with the variable elimination algorithm by taking advantage of two ideas. The first idea is that we are going to make use of the idea of dynamic programming. So when we're calculating probabilities, a lot of the results of intermediate calculations, we will use it multiple times. So in order to save time, we'll do those small intermediate calculations first, save the results, and then use the results later. So this avoids repeated calculations. And the second idea we're going to make use of is that we are using the Bayesian network, and the Bayesian network already explores conditional independence so that we need to specify fewer probabilities. So by making use of the Bayesian network and exploiting the conditional independence, we can do less work than directly using the joint distribution. Before we look at the details of the variable elimination algorithm, let me summarize it in one sentence. In the variable elimination algorithm, we will first define factors, then we will perform operations on these factors to get the results of the probabilistic inference that we want. I will first define what is a factor, and then after that I will discuss four different operations we can perform on factors. Restrict, sum out, multiply, and normalize. Let me conclude for this video. After watching this video, you should be able to do the following. Explain why we should not hope for a general efficient implementation of probabilistic inference for a Bayesian network. Explain why the variable elimination algorithm can be more efficient than the naive approach for exact inference. What ideas does the algorithm take advantage of to achieve the improved efficiency? Thank you very much for watching. I will see you in the next video. Bye for now.